energy consumption modelling in the injection moulding ... · moulding is a relatively low energy...
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Energy Consumption Modelling in the Injection Moulding
Industry
Gonçalo Nuno Alfredo Cardeal
Thesis to obtain the Master of Science Degree in
Mechanical Engineering
Supervisors: Prof. Inês Esteves Ribeiro
Prof. Paulo Miguel Nogueira Peças
Examination Committee
Chairperson: Prof. Rui Manuel dos Santos Oliveira Baptista
Supervisor: Prof. Inês Esteves Ribeiro
Members of the Committee: Prof. Elsa Maria Pires Henriques
Doctor António José Caetano Baptista
November 2016
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Acknowledgments
I would like to express my sincere gratitude to Prof. Inês Ribeiro and Prof. Paulo Peças for supporting
me and this thesis with their time, knowledge and most important, understanding during the development
of this dissertation.
I would like to thank the company that opened its doors to me and allowed the completion of my master
thesis. I would like to thank specially Eng. Filipe Santos, José Lopes and Francisco for their assistance
during the time I spent in the company, and for their guidance and friendship.
Also, I would like to thank my colleagues, Diogo, João, Rita, Maria, Rafael, Manuel, Vasco and Tiago
for the companionship during this journey. I would like to thank in particular Ana Rita for the help,
companionship and collaboration regarding the last part of both our thesis.
To my family I would like to thank specially for the patience and support given not only during this thesis
but during all of my studies. Thank you for the patience, encouragement and attention.
Last but not least, I would like to thank Barbara for all the support during the last few years and especially
during the development of this dissertation, for giving me the courage I needed to complete this journey.
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Abstract
The injection moulding process is a large-scale process at a global level, and therefore leads to a high
environmental and economic impact. Because of this large scale, small efficiency improvements may
lead to large energy savings. In general, the injection moulding companies are focused on energy
consumption and their main goal is to reduce energy in order to achieve a sustainable manufacturing.
Several models have been developed to predict energy consumptions in different industries, and plastic
injection is no different. Specific energy consumption models, SEC, are used throughout the industry
and process based models have been developed to account for the properties of the injection moulding
process. Nevertheless, the scarce of data inhibit those models to estimate the energy consumption with
the required accuracy to be assumed as validated to a wide universe of technological and production
contexts.
This thesis evaluates different models to estimate the energy consumption in injection moulding parts
production based on an extensive data collection set in industrial environment. SEC and process based
models are developed and extensively tested with the data gathered in industrial environment.
Further, it was developed a model to estimate energy consumptions in the injection moulding industry
based on neural networks. This model takes advantage of the extensive industrial data gathered and
uses a carefully selected group of parameters as inputs to obtain estimations of the energy consumed
in the process.
Keywords: Sustainable Manufacturing, Energy Consumption, Injection Moulding, SEC, Process Based
Model, Neural Network
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Resumo
O processo de injeção de plástico envolve grandes volumes produtivos a nível global, resultando em
consideráveis impactos económicos e ambientais. Como resultado da grande escala, pequenas
melhorias na eficiência do processo podem conduzir a significantes poupanças de energia. Em geral,
as empresas envolvidas nesta área produtiva mostram grande preocupação com os consumos de
energia, e o seu principal foco é a redução da energia consumida para atingir uma produção mais
sustentável.
Diversos modelos foram desenvolvidos com o objetivo de estimar consumos energéticos em diferentes
processos e a injeção de plásticos não é exceção. Modelos de energia específica são vastamente
utilizados em diversos processos, incluindo a injeção de plásticos, e vários autores desenvolveram
modelos do processo para contabilizar as particularidades deste processo. Contudo, a diminuta
quantidade de dados experimentais utilizados no desenvolvimento das abordagens existentes significa
que os modelos propostos não podem ser assumidos como validos perante as comunidades científicas
e industrial.
Esta tese analisa diferentes modelos de estimativa de consumos de energia na indústria de injeção de
plásticos com base numa vasta base de dados recolhidos em ambiente industrial. São desenvolvidas
e testadas abordagens de consumo específico de energia, SEC, bem como um modelo do processo,
PBM, utilizando os dados recolhidos.
Adicionalmente, é proposto um modelo de estimativa de consumos de energia baseado em redes
neuronais. Este modelo beneficia da extensa base de dados recolhida e utiliza um número bem
estudado de variáveis de entrada para propor estimativas de energia consumida.
Palavras-Chave: Produção Sustentável, Consumo de Energia, Injeção de Plástico, SEC, Modelo do
Processo, PBM, Redes Neuronais
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Contents
Acknowledgments ................................................................................................................................. ii
Abstract ................................................................................................................................................. iii
Resumo.................................................................................................................................................. iv
Nomenclature ......................................................................................................................................... x
1. Introduction .................................................................................................................................... 1
2. Injection Moulding ......................................................................................................................... 3
2.1. Origins and Growth ................................................................................................................ 3
2.2. Process Description ............................................................................................................... 4
2.2.1. Machines Used ......................................................................................................... 4
2.2.2. Process ..................................................................................................................... 5
2.3. Energy Consumption in Injection Moulding ........................................................................... 6
3. Energy Modelling ........................................................................................................................... 8
3.1. Overview ................................................................................................................................ 8
3.2. Energy consumption modelling in injection moulding ......................................................... 13
3.2.1. Thermodynamic models ......................................................................................... 14
3.2.2. Machine models ...................................................................................................... 15
3.2.3. Dual Model .............................................................................................................. 15
3.2.4. SEC models ............................................................................................................ 16
3.2.5. SEC vs Throughput ................................................................................................ 17
3.2.6. Artificial Neural Networks........................................................................................ 18
4. Methodology ................................................................................................................................. 20
4.1. Literature Revision ............................................................................................................... 21
4.2. Experimental work ............................................................................................................... 21
4.3. Model development ............................................................................................................. 22
5. Energy consumption analysis .................................................................................................... 24
5.1. Measuring Equipment .......................................................................................................... 24
5.2. Measuring method ............................................................................................................... 25
5.3. Experimental data ................................................................................................................ 29
5.4. Data analysis ....................................................................................................................... 31
5.5. Causes of the variation of energy consumptions for similar processes .............................. 35
vi
6. Model Development ..................................................................................................................... 38
6.1. Specific Energy Consumption ............................................................................................. 38
6.1.1. SEC vs Material type .............................................................................................. 39
6.1.2. SEC vs Clamping Force ......................................................................................... 41
6.1.3. Combined Data ....................................................................................................... 44
6.2. Process-Based Model ......................................................................................................... 46
6.2.1. New coefficients ...................................................................................................... 50
6.3. Neural Networks .................................................................................................................. 52
7. Discussion .................................................................................................................................... 60
7.1. SEC Model .......................................................................................................................... 60
7.2. Process-Based Model ......................................................................................................... 61
7.3. Neural Networks Model ....................................................................................................... 62
8. Conclusion ................................................................................................................................... 63
9. Future Work .................................................................................................................................. 65
10. References .................................................................................................................................... 66
11. Annex ............................................................................................................................................ 69
11.1. Annex I- Data Gathering Sheet ........................................................................................... 69
11.2. Annex II- Machine List ......................................................................................................... 70
11.3. Annex III- Experimental data ............................................................................................... 73
11.4. Annex IV- Matlab Code ........................................................................................................ 84
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List of figures
Figure 2.1- Schematics of a typical injection machine [7] ....................................................................... 4
Figure 2.2- Energy consumed during an injection moulding cycle by a hybrid (electric screw drive) and
an all-electric machine [3] ........................................................................................................................ 5
Figure 2.3- Contribution of the different machine parts in the energy consumption ............................... 6
Figure 3.1- Graphic showing the evolution of the specific energy consumption with the variation in
throughput [27] ...................................................................................................................................... 10
Figure 3.2- Energy balance “approach” [12] .......................................................................................... 16
Figure 3.3: Energy used in an automobile machining line as function of production rate [27] .............. 17
Figure 3.4: SEC vs Throughput [28] ...................................................................................................... 18
Figure 3.5 Schematic of a multi-layer artificial neural network [34] ....................................................... 19
Figure 4.1- Scheme of the methodology chosen to approach the thesis .............................................. 20
Figure 5.1- Equipment used to measure energy consumptions, PROVA 6830 .................................... 24
Figure 5.2- Instalation of equipment A ................................................................................................... 25
Figure 5.3- Example of the consumption profile obtained with method A ............................................. 26
Figure 5.4- Detailed view of a power consumption graphic (Method A) ................................................ 27
Figure 5.5- Detailed view of the different stages of the injection cycle ................................................. 27
Figure 5.6- Example of power usage graphic obtained by method B ................................................... 28
Figure 5.7- Influence of the machines installed power on the energy consumption ............................. 31
Figure 5.8- Close up of the influence of the machines installed power on the energy consumption .... 32
Figure 5.9- Influence of the cycle time on the energy consumption ...................................................... 32
Figure 5.10- Influence of the injected mass on the energy consumption .............................................. 33
Figure 5.11- Detail of the influence of the injected mass on the energy consumption .......................... 34
Figure 5.12- Relation between the energy consumption and the maximum thickness of the injected part
............................................................................................................................................................... 34
Figure 5.13- Power consumption profile for machine 58 with and without the VFD ............................. 36
Figure 6.1- Graphic showing the relation between the specific energy consumption of the measures took
and the throughput for each case. ......................................................................................................... 38
Figure 6.2- Graphic showing the relation between the specific energy consumption of the measures took
and the throughput for each case, highlighting the different materials used. ........................................ 40
viii
Figure 6.3- Graphic relating the specific energy consumption of the different experimental cases with
the correspondent machines clamping force. The average SEC value for each case is highlighted. .. 41
Figure 6.4- Graphic showing the SEC vs Throughput for the data from companies A, B, C ................ 45
Figure 6.5- Table showing the specific energy consumption for the measures taken in the three
companies group by the machines clamping force ............................................................................... 46
Figure 6.6- Evolution of the estimated machine power coefficient with the ratio between the
thermodynamic power and the installed power. .................................................................................... 51
Figure 6.7- Correlation between the target and output for the training, test and overall sets ............... 56
Figure 6.8- Correlation between the target and output for the training, test and overall sets for the
combined data set ................................................................................................................................. 58
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List of tables
Table 3-1- Comparison of the models found in the literature ................................................................ 13
Table 5-1- Distribution of the different machines by clamping force ...................................................... 29
Table 5-2- Material properties used for modelling energy consumptions [40] ....................................... 30
Table 5-3- Example of the data gathered .............................................................................................. 30
Table 5-4- Comparison of the average power consumption between the same machine, using VFD's
and working in normal condition. ........................................................................................................... 37
Table 6-1- Correlation of the curve relating each material specific energy consumption and its throughput
............................................................................................................................................................... 40
Table 6-2- Average values of specific energy consumption [MJ/kg], sorted by clamping force [ton] .... 42
Table 6-3- Average values of specific energy consumption [MJ/kg], sorted by clamping force [ton] .... 43
Table 6-4- Distribution of the data used ................................................................................................. 44
Table 6-5- Inputs and outputs used in Ribeiro et al model [12]. ............................................................ 47
Table 6-6- Example of the average error obtained using the selected PBM model .............................. 49
Table 6-7- Inputs and output used in the ANN learning process ........................................................... 52
Table 6-8- Results of the experiments for the first training set, {70,15,15} ........................................... 54
Table 6-9- Results of the experiments for the second training set, {60,25,15} ...................................... 55
Table 6-10- Results of the experiments for the third training set, {50,25,25} ........................................ 55
Table 6-11- Key variables used in the model ranked in terms of influence in the results...................... 56
Table 6-12- Results of the experiments for the first training set, {70,15,15} [Combined data] .............. 57
Table 6-13- Results of the experiments for the first training set, {60,25,15} [Combined data] .............. 57
Table 6-14- Results of the experiments for the first training set, {50,25,25} [Combined data] .............. 58
Table 6-15- Comparison of the MSE and standard deviation results in both the single and the combined
data set .................................................................................................................................................. 59
x
Nomenclature
SEC Specific Energy Consumption
PBM Process Based Model
NN Neural Network
ABS Acrylonitrile Butadiene Styrene
POM Polyoxymethylene
PP Polypropylene
PS Polystyrene
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1. Introduction
Plastic is one of the materials more used in the manufacturing industry and injection moulding is the
main process responsible for the manufacturing of plastic components and products. Plastic injection
moulding is a relatively low energy process, however, the large scale of this process at a global level
means that there are significant amounts of energy being spend in this process, thus, an apparently low
reduction in energy consumption can lead to significant power savings.
The topic of energy saving is increasingly important at a global scale. A reduction in the amount of
energy consumed in large scaled processes throughout the industry can lead not only to significant
reductions in the cost of the final product, but also to a significant positive impact on the environmental
crisis that is affecting the worlds nowadays. In fact, most companies have already put in motion
environmental plans intending to reduce the impact caused by their production to achieve sustainable
production. Most of this plans include energy monitoring and planning as this is a preponderant factor
to achieve environmental sustainability and sustainable production.
The topic of energy impact reduction is already approached by monitoring and controlling the patterns
of energy consumption during the process. However, the most common practice for this objective is to
act in the planning stage of the process. Throughout the industry, products are planned and designed
with the objective of achieving the lowest possible energy consumption. This is, in fact, the best time to
approach the problem, as decision and options made during the design stage of the part/process can
difficult the process of reducing energy consumptions during the manufacturing of the product. To
improve the results obtained in this stage and reach the process with the lowest impact possible it is key
to properly estimate energy consumptions.
In this thesis it is presented an extensive analysis of the models available to estimate energy
consumptions in the injection moulding industry. A group of models were selected to be tested,
developed and validated using a comprehensive data set gathered in industrial environment using
coherent techniques and methodologies.
The first chapter introduces the theme and provides a small description of the document.
The second and third chapters present the review of the existing literature, the selection of the relevant
models to test and develop and the identification of the key variables in energy consumption.
In the fourth chapter is presented and described the methodology used to approach this thesis.
The fifth chapter introduces the data gathered in the visited company. In this chapter the data is
presented in detail and a study of the influence of each monitored parameter is shown. There is also
presented a combination of information gathered in the company regarding different process and
machines characteristics and relating them to the energy consumption.
Different approaches are tested in chapter six for the most commonly used method for predicting energy
consumptions, the SEC model. This metric is capable of producing estimations without requiring great
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knowledge of the process and its conditions. With only one input energy consumptions can be estimated.
This metrics is developed using different approaches, first a generic SEC value is calculated and
posteriorly the machines and materials are grouped to obtained new values.
This chapter also presents an analysis of applicability of a process based model to the case study
mounted. This analysis consists of the validation of the previously presented model and the further
development of the same model.
Lastly, the sixth chapter presents the development and testing of a neural network model for estimating
energy consumptions. This model is sensitive to a large number of processing parameters as well as
machine and material used. It is relatively simple and fast to use because it works similarly to a black
box, in a way that it does not require the understanding of the process behind the model.
The seventh chapter provides a discussion of the results obtained during the thesis. In this chapter the
limitations/advantages of each tested model are pointed out.
Chapter eight is composed by the conclusion of the work developed. This chapter compares and places
the different models observed during this thesis.
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2. Injection Moulding
Plastic injection moulding is a process widely used in global level. Plastic injection industry is becoming
more important in recent years, being used in different sectors throughout the market.
In the present day, plastic is a widely use material and injection moulding is a process responsible for a
considerable part of the plastic used. It is estimated that 32% of the plastic used in the manufacturing
industry comes from injection moulding [1].
According to Fisher et al. [2], plastics have a significant presence in several different industries. In 2000
it was estimated that 42% of the produced toys constitution was plastic, small house appliances were
constituted 33% by plastic and that monitoring and control systems were 38% made out of this material.
Plastic injection moulding is a fairly inexpensive process that can be used in a large scale and allows
the manufacturing of complex parts maintaining high quality standards, there for, most of the plastic
used in this cases results of injection moulding processes [3].
This chapter presents a brief history of injection moulding as well as a small discussion of the process
and its components.
2.1. Origins and Growth
Injection moulding first appeared in the late 1800’s and it was used to produce medical appliances, small
buttons and components for the aerospace industry [4]. The process began to grow until 1930s when
the major development in vinyl thermoplastics begun. In 1946 the first injection machine using a screw
mechanism was invented and this configuration it’s used to this date in 95% of the machines [5]. With
this new structure of machine, the industry continued to grow and more materials were introduced.
Latter, in 1979 the plastic production overtakes the steel production and just six years after, in 1985 the
first electric machine is invented. Today, plastic moulding is one of the main manufacturing technics, and
in the UK alone it evolved from an 18 million pound (2002) business to a 3,2 billion pound in the modern
days [5].
In Portugal, both the injection moulding industry and the moulding making industry are preponderant.
The mould making industry is growing and counts with 532 companies distributed over two major areas,
employing a total of around 8250 workers. This numbers might appear small, but Portugal is amongst
the major mould makers in the world, exporting 90% of the annual production to country’s such as
Germany, USA, France, amongst others [6].
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2.2. Process Description
2.2.1. Machines Used
In the modern injection moulding industry, 95% of the machines used are based on a screw mechanism
[5]. This type of machine is constituted by two different units, the injection, and the clamping unit, as
shown in Figure 2.1.
Figure 2.1- Schematics of a typical injection machine [7]
The two units present in the constitution of injection machines serve different purposes during the
injection process.
1. Injection unit: This part of the machine is responsible by injecting the material and maintaining
the pressure during the injection. In order to achieve this, the injection unit is responsible for
receiving the material, melting the polymer, injecting it into the mould cavity and maintaining the
pressure.
2. Clamping unit: The clamping unit is the part of the machine responsible for closing the two sides
of the mould and by maintaining the pressure during the process. It’s also responsible for
opening the face of the mould to extract the injected part.
There are currently three different types of injection machines using the screw system, [8]. Each one
presenting different energy profiles. The most commonly found machine is the hydraulic, but there are
also hybrid and electric machines. Hydraulic machines are the most significant in terms of energy
consumption,[8], their method of functioning requires hydraulic pumps in order to assure the large
movements of oil involved. Therefore, hydraulic machines display large quantities of energy spent in
idle, reducing overall efficiency of the process. The biggest advantage of hydraulic machines is the large
capacity and clamping force that those machines are able to achieve.
5
Hybrid machines differ from hydraulic because they have either, an electric clamping unit and a hydraulic
injection unit, or the opposite. This configuration allows to maintain some desired capacity properties of
their hydraulic equivalents but reducing energy consumptions.
The most efficient type of machine in terms of energy is the electric. Being all-electric, it works with servo
motors actuating only when needed, resulting in significant saving, mainly because it eliminates idle
power consumptions.
Figure 2.2- Energy consumed during an injection moulding cycle by a hybrid (electric screw drive) and an all-electric machine [3]
2.2.2. Process
Plastic injection moulding is a process capable of mass production, because of the repeatability it
guarantees due to its cyclic nature. This process occurs in cycles, beginning with the entrance of the
material in the machine and ending with the extraction of the injected part. The cycle that results in an
injected part is composed of a series of smaller stages, as listed below:
1. Material entrance stage- The polymer enters the injection machine from the feeder. Feeding
systems are fundamental to the injection and usually work in parallel with the machine. This
systems are usually constituted by a silo (used to store the material), a series of tubes and a
pump to move the polymer thru the tubes and into the injection machine.
2. Melting stage- Once inside the barrel of the machine, the material is heated to its fusion
temperature, becoming a liquid. This is achieved by a variable number of heaters mounted in
series around the barrel of the machine. During this stage the material is being continuously
pushed to the interior of the mould, starting the filling stage.
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3. Filling stage- In this phase the material is pushed to the interior of the moulds cavity.
4. Compaction stage- This phase marks the beginning of the post filling. During this time, the
material is already inside the cavity, and pressure is applied to the mould.
5. Cooling stage- The cooling stage is marks the final transformation to the material. During this
time, mould is cooled by a series of interior channels, resulting in a decrease of the material
temperature. This is the longest part of the injection cycle, it is estimated that the cooling stage
takes between 50 to 80% of the cycle time [9].
6. Unmoulding stage- The cycle ends with the unmoulding or extraction of the injected part. In this
stage, the clamping cylinder pushes the movable plate to open the mould, thus realising the
injected part. In some cases the extraction requires a robotic arm, this is usually used in more
complex parts, or when the machine is connected to an assembly line.
2.3. Energy Consumption in Injection Moulding
Plastic injection moulding is a complex process in terms of energy consumption. There are several
conditions involved in the power required by the injection machine to produce a certain part.
Injection moulding machines are composed by different components, as described before. Injection
moulding is a process that depends not only of the injection machine but also, of its peripherals. Cooling
unit, feeding units, mould heating units and plasticizing units consume energy during the process. Some
studies show the distribution of the energy spent during an injection cycle by the average hydraulic
injection machine, [10], [11].
Figure 2.3- Contribution of the different machine parts in the energy consumption
1%
17%
33%32%
15%
2%
Robot Plasticizing unit heating
Mould temperature regulation Injection moulding machine drive
Peripheral equipment Injection moulding machine control
7
Figure 2.3 shows the distribution of the energy spent by each unit involved in the process. Looking at
the graphic it is evident that the main part of the energy spent is consumed by the injection moulding
machine drive and by the mould temperature regulation. It is also evident that peripherals such as the
robot and the machine control are irrelevant in terms of consumption [11].
Ribeiro et al. [12], developed a model to estimate energy consumption, that will be explained in the next
chapter. During the development of this model the authors gathered experimental data, more
specifically, energy consumptions and processing conditions. That data was used to see the influence
of several parameters in the energy consumption. The parameters defined by the authors as key in
influencing energy consumptions are the following:
1. Installed power- The installed power of the injection machine is related with its dimension and
its expected that higher values of installed power results in highest energy consumptions,
2. Cycle time- The longer the duration of the cycle, the longer the machine is consume energy,
therefore, an increase in this variable results in higher consumptions,
3. Part maximum thickness- Empirical models to estimate the cooling time assume that the
maximum thickness of the part is directly related to this aspect [13], [14]. As said before it is
known that the cooling time constitutes 50 to 80% of the cycle time [9], therefore the increase
in the maximum part thickness leads to an higher energy consumption, as verified by [12],
4. Mass of injected material- More mass usually means a bigger part and thus, a bigger machine
and therefore an increase in energy consumption,
5. Material properties- Properties such as the melting temperature of the injected polymer,
diffusivity, density, specific heat and injection pressure are directly related to the energy
consumption as they influence the parametrization of the process,
A further analysis around the influence of the maximum thickness was conducted by Domingues [15].
In this work the author test the influence of the parts thickness on the energy consumption for a larger
set of data than the one used before, [12]. With this analysis, the author concluded that there is not
necessarily a direct relation between this factors. It was expected that thicker parts take more time to
cool than thinner ones, resulting in higher cycle times, thus increasing the energy consumption.
This hypothesis is supported by Stelson [16] who studied the same influence and came to the conclusion
that thinner parts might require higher cooling time due to their geometry. According to the author,
imperfect thermal contact between the mould cavity and the injected part can lead to errors in the
theoretical approximations. This work also points to the fact that for thicker parts the theoretical
approximation [13], [14], will predict longer times than reality, this happens because the ejection is also
related with the stiffness of the part. Thicker parts are stiffer, thus they can be ejected with higher
temperatures than thin parts, resulting in a miss estimation of the theoretical models, [13], [14].
8
3. Energy Modelling
As said before, injection moulding is a large scale process used widely and constitutes a significant part
of the worlds manufacturing, therefore, a small reduction in the energy consumption of this process can
lead to significant energy saving in a global scale. This can cause a big impact on the environmental
crisis caused by the pollution and scarcity of current resources.
In this chapter there will be presented some approaches to model energy consumptions in different
manufacturing processes, with examples of applications for those models based on an extensive
literature review. There will also be displayed a more detailed analysis of the models used in injection
moulding, as well as their strong and week points.
3.1. Overview
Over the last few years there has been an ever-growing concern about the environmental crisis.
Although the concern for obtaining electricity using greener methods is growing, this practice is still far
from being the main source of energy. In 2013, the World Energy Council for Sustainable Energy
estimated that around 82% of the energy requirements were met by fossil sources, and only 13% of the
energy was generated using clean methods [17].
The problem of the world energy crisis is being extensively studied throughout the scientific community.
Sustainable decision making tools are found on several papers addressing energy consumptions and
energy saving methodologies in the manufacturing industry. It is very common to analyze the problem
of eco efficiency and sustainable production in terms of life cycle assessments and the life cycle cost
[18], [19]. Both this analysis appear as a tool to help decision making throughout the product’s life in
terms of eco-efficiency and sustainable production. Life cycle analysis models allow the industry to
evaluate the environmental and cost impacts of every step of the product life and its overall significance
in the full life cycle.
Extensive analysis on predicting energy consumptions have been developed in the past few years. A
great number of models were developed to estimate consumptions in a large spectrum of industrial
processes. The most commonly developed models can be sub-divides into three main categories:
1. SEC- Specific Energy Models, this type of models intends to relate the energy consumption of
a certain process with one of its characteristics,
2. PBM- Process Based Models, this type of models intends to estimate energy consumptions by
characterizing closely the different parts and aspects of the process,
9
3. Empirical Models- This type of models, estimates energy consumptions using mathematical
formulations based on the physical and chemical properties of the process, this models are
developed using experimental data,
Recently, in 2016, Zhou et al. [20], compiled an extensive literature review on the subject of energy
consumption modelling and energy efficiency of machine tools. In this analysis the authors compiled
models from several different authors, with models to estimate both the efficiency of machining
processes and their energy consumptions. Looking at this article it’s evident that a big part of the
modelling being done in this area is achieved using SEC models.
Many authors use specific energy consumptions models to estimate energy consumptions, however the
majority does it for machining processes and just a few used this method for injection moulding. Recently
some authors developed SEC models for machining processes using the material removing rate as an
input, [21]–[23].
Duflou et al. [22] studied the effects of the optimization of the process control in the milling industry, and
for the energy related part of his studies used SEC models. With this work, the author concluded that
SEC technics can be really useful for reducing energy consumptions in the milling process.
Balogun et al. [24], developed a SEC model comparing the specific energy consumption with different
cutter swept angle. This model was developed to evaluate the relation between the cutter angle and the
specific ploughing energy. The proposed methodology allowed the authors to identify the optimum angle
to achieve the desired value of energy consumption in the milling process.
Benchmarking efficiency is a powerful tool to help modify practices and simplify sustainable decision
making in manufacturing processes [25]. Recently, a research team [25] proposed an energy efficiency
label for the specific case of injection moulding in the automotive industry. This label contains information
about the mould and the machine used, and provides estimations of the energy required, obtained using
a SEC model developed by the authors.
Some authors are working on improving traditional SEC models. Li et al. [26] developed an improved
SEC model to predict and evaluate relations between the energy consumption of the milling process
and several different aspects related to it. The proposed model is based on an empirical background
and uses coefficients obtained by a statistic analysis of experimental data. The coefficients used,
account for different machine tools and with this model the authors were able to achieve energy
estimation with 97% of average accuracy.
SEC models are most often used for predicting energy consumptions in machining process such as
10
milling and turning. However, some authors, [27], done extensive research on this models for a wider
range of manufacturing processes. Gutowski et al. [27], proposed SEC values for different experimental
cases in a wide range of processes, such as machining, grinding, waterjet and most importantly for this
case, injection moulding. The author intends to prove with this work that the process rate is the most
important parameter when it comes to influencing the specific energy consumption. This article also
compares the specific energy consumption of an all-electric injection machine versus a hydraulic one,
concluding that redesigning the machines can be very useful way to reduce energy consumptions.
Figure 3.1- Graphic showing the evolution of the specific energy consumption with the variation in throughput [27]
Thiriez, [28], developed a more detailed analysis of the effects of the machines powering method using
SEC models. In this article the authors compare electric injection machines with hydraulic (electric screw
drive) and evaluate the impact of each one in a life cycle analysis.
Specific energy consumption models are obviously predominant in energy estimation. However, some
authors developed approaches to model energy consumptions in machining processes using
mathematical models. Liu et al. [29], developed a model to estimate energy consumptions in machining
processes. The model is based on the idea that the energy spent during the process can be divided into
three parts, start-up periods, idle periods and cutting periods. The first two periods are obtained using
experimental data, by looking at the curve representing the energy consumption as function of the
speed. The last part of the model, the cutting period, is obtained by using the power required based on
the cutting parameters. The model was validated with a practical case study, and the author manage to
obtain low values of error, around 8%.
Balogun and Mativenga [30], presented an article were a mathematical model was developed to
estimate energy consumptions considering once again three states of the machine. The basic and ready
state power and the cutting power. The requirements in terms of power for the different states in various
conditions are calculated using experimental data.
11
He et al. [31] proposed a model to estimate energy consumptions in CNC machining processes based
on the physics involved in milling and turning. The model divides the energy consumption in various
parts and determine each value theoretically using the expected power required based on processing
conditions.
Zhang et al. [32] proposed a method to reduce energy consumption by improving planning and
scheduling in turning. The authors propose a framework in which the energy of each machine used is
accounted in both the working condition and in idle. The model then proposes based on the energy in
both states the best planning for the process in terms of energy efficiency. For this goal the authors
calculate the energy consumption based on a mathematical model.
Process based models are expected to characterize the process stages and their processing conditions.
Some works use PBM approaches to estimate energy consumptions in injection moulding [12], [33].
Ribeiro et al. [12], developed a process-based model to estimate energy consumptions in injection
moulding. The authors propose a model that considers a larger set of inputs than the one taken into
account in the rest of the works. Characteristics such as the geometry of the injected part, cycle time,
machine properties and others are integral parts of this model. The model is sensitive to processing
condition, material type and geometry of the part, and it was developed using empirical relations and a
set of experimental data. It uses specific coefficients to account for different parameters, obtained using
a static analysis of the experimental data.
Kalogirou [34], outlined various applications of artificial neural networks for modelling energy
engineering systems, showing the wide variety of applications for such methods. According to the author
they are used to predict and forecast different parameters.
Neural networks based models are commonly found when modelling energy consumption in buildings.
Khosravani et al. [35], compared models using artificial neural networks to predict energy consumption
of bioclimatic buildings and concluded that this methods are very promising but require a considerably
large data set to be properly developed.
Karatasou et al. [36] extensively studied the development of neural networks model to predict energy
consumptions. The research group conducted a series of experiments pointing the importance of
selecting the right input variables and the proper parametrization of the network.
Table 3-1 contains a compilation of the models and approaches discussed in this chapter, for comparison
of the work developed in the field.
12
Injection
moulding Machining Others Description
Duflou et al.
[22] x SEC model with the material removal rate
Balogun et al.
[24] x
SEC model with the material removal rate
for different cutter angles
Siering et al.
[25] x
Energy efficient label with energy
estimations using a SEC model with the
throughput
Li et al. [26] x
SEC model with the material removal rate
and coefficients for various processing
parameters
Gutowski et al.
[27] x x x
SEC indicators for different processes
using the throughput
Thiriez et al.
[28] x
SEC for different types of injection
machines
Liu et al. x Empirical model based on the different
states of the process
Balogun and
Mativenga [30] x
Empirical model based on the different
states of the process
Li and Kara [21] x Empirical model to predict energy
consumptions
Ribeiro et al.
[12] x
Process based model sensitive to
processing condition, material condition
and machine properties
13
He et al. [31] x Uses physical properties of the process to
estimate energy consumptions
Karatasou et
al. [36] x
Neural network model to predict energy
consumption in buildings
Table 3-1- Comparison of the models found in the literature
Several models have been developed to estimate energy consumptions in different processes, however,
all of them are based, either, in relatively small sets of data, or are purely theoretical and do not use
any experimental measurements. Most models would benefit from being tested in an industrial
environment, using large sets of data including wide ranges of different machines, parts and processing
conditions.
Specific consumption models appear particularly useful when estimating energy consumptions and are
the most used in different processes.
Process based models such as the one developed by Ribeiro et al. [12] provide a faithful
characterization of the injection process, having a significant number of inputs. However, its precision is
still unknown for larger sets of data, and there appears to be room to test and develop it further.
Neural networks models appear to be a valid tool to estimate energy consumption, [34], but are still to
be applied to the injection moulding industry.
3.2. Energy consumption modelling in injection
moulding
As seen before most of the work done recently in terms of energy consumption modelling is applied to
machining processes such as milling and turning. There are, however, some models to estimated energy
consumptions in injection moulding. All of the three main types of models presented before were found
for the injection moulding case, and a more detailed analysis of each one in presented below
14
3.2.1. Thermodynamic models
Every injection moulding process requires thermodynamic energy. Some authors proposed models
based on thermodynamic fundamentals in order to predict energy consumptions during plastic
injection[28], [33]. During the injection moulding process thermodynamic energy is required to melt the
polymer and later to inject it into the mould cavity. Based on this knowledge, this model formulation
would be the one seen on equation (1).
thermo melt fillE E E ( 1 )
Where the first parcel, 𝐸𝑚𝑒𝑙𝑡, represents the energy used to melt the polymer and the second, 𝐸𝑓𝑖𝑙𝑙, is
the energy used to fill the mould cavity.
Being this a model based on the fundamentals of thermodynamics, it’s possible to calculate the energy
necessary to melt the polymer, 𝐸𝑚𝑒𝑙𝑡, to a certain degree of error. This amount of energy depends on
whether the polymer is crystalline or non-crystalline. For both cases the amount of energy necessary
can be expressed according to the fundamentals of thermodynamics [equation (2)].
𝐸𝑚𝑒𝑙𝑡 = {𝑚𝑐𝑝(𝑇𝑚𝑒𝑙𝑡 − 𝑇𝑎𝑚𝑏), 𝑓𝑜𝑟 𝑛𝑜𝑛 − 𝑐𝑟𝑦𝑠𝑡𝑎𝑙𝑙𝑖𝑛𝑒 𝑝𝑜𝑙𝑦𝑚𝑒𝑟𝑠
𝑚𝑐𝑝(𝑇𝑚𝑒𝑙𝑡 − 𝑇𝑎𝑚𝑏) + 𝜆𝑚𝐻𝐹 , 𝑓𝑜𝑟 𝑐𝑟𝑦𝑠𝑡𝑎𝑙𝑙𝑖𝑛𝑒 𝑝𝑜𝑙𝑦𝑚𝑒𝑟𝑠 ( 2 )
Where m is the mass of the injected material, 𝑐𝑝 is the polymer specific heat, 𝑇𝑎𝑚𝑏 is the ambient
temperature, 𝑇𝑚𝑒𝑙𝑡 is the melting temperature of the polymer. The term 𝜆𝑚𝐻𝐹 used for crystalline
polymers represents the energy needed to transform the polymer crystalline structure to the fluidic
disorganized structure, where 𝜆 is the degree of crystallisation and 𝐻𝐹 is the heat fusion for a 100%
crystalline polymer. The energy required to melt the polymer,𝐸𝑚𝑒𝑙𝑡, is the main parcel of the energy
required for the processes, 𝐸𝑡𝑜𝑡𝑎𝑙, [28].
The energy needed to fill the mould cavity, 𝐸𝑓𝑖𝑙𝑙 , unlike the previous, depends on the form and dimension
of the mould cavity and runner system, thus it is impossible to formulate this parcel accurately. Therefor
it’s used a simplified formulation to illustrate this amount of energy [equation (3)].
fill injE pdV pV ( 3 )
Where p is the instantaneous pressure, V is the volume. This equation is obtained by integrating the
instantaneous pressure, p, in each volume increment, V. To simplify the problem caused by the diversity
15
of mould and runner systems, equation (3) can be simplified using the average pressure, �̅�, and the
volume of injected material, 𝑉𝑖𝑛𝑗.
3.2.2. Machine models
As seen previously, thermodynamic models only account for the amount of energy spent on melting and
filling processes. Even though those processes represent most of the energy consumption, sometimes
it’s necessary to account for the energy used by the machine in the rest of the cycle. Therefor the
machine model includes the thermodynamic and adds to it a component related to the machine, [28],
[33], [equation (4)].
𝐸𝑡𝑜𝑡𝑎𝑙 = 𝐸𝑚𝑒𝑙𝑡 + 𝐸𝑓𝑖𝑙𝑙 + 𝐸𝑝𝑎𝑐𝑘 + 𝐸𝑐𝑙𝑎𝑚𝑝 + 𝐸𝑒𝑗𝑒𝑐𝑡 ( 4 )
Equation (4) first two terms, 𝐸𝑚𝑒𝑙𝑡 and𝐸𝑓𝑖𝑙𝑙, are obtained from the thermodynamic model, and the others
relate to the machine model. 𝐸𝑝𝑎𝑐𝑘 is the energy needed for the packing stage, 𝐸𝑐𝑙𝑎𝑚𝑝 is the energy
used to clamp the mould and 𝐸𝑒𝑗𝑒𝑐𝑡 is the amount of energy needed to eject the part from the mould.
The last three terms of the equation are often ignored, as a simplification, based on the fact that the
energy required to clamp, pack and eject only accounts for 25% of the total usage [33].
The energy required for packing, clamping and ejecting is also not easy to evaluate as it depends greatly
on the mould characteristics, as well as on the machines power and it size.
3.2.3. Dual Model
The model proposed by Ribeiro et al. [12], takes advantage of the two previously described model. It is
based on an energy balance, in which the total energy consumption can be obtained by combining two
of the previously explained models, the thermodynamic and the machine model. In this model, the
authors propose to solve the difference between different properties and characteristics of the processes
by using specific coefficients.
Unlike some other models found in the literature, this one is based on an extensive use of experimental
data in order to be developed and validated. In its development stage, this model had significant
industrial data input, to achieve this, the authors [12] evaluated 11 different energetic consumptions,
with varying parameters and working conditions. By doing so, it was possible to understand the effect
of the various factors that determine energetic consumptions in this process. Every measurement used
in this stage was obtained by utilizing the same measure equipment in order to assure the scientific
relevance of the data.
16
Figure 3.2- Energy balance “approach” [12]
This models methodology is illustrated in Figure 3.2, as mentioned above, the model consist of two
parts. The first one, obtained in literature, considers the impact caused by part design and process
conditions (average pressure and temperatures) but fails to account for the machine characteristics and
the production cycle time. Thus, the authors added a second parcel, the machine model. This parcel of
the energy balance, allows the model to consider the influence of the machine type (electric vs
hydraulic), machine installed power and part geometry. For each one of these parameters a specific
coefficient is used, in other to account their individual impact.
3.2.4. SEC models
Specific energy consumption (SEC) models are widely used to evaluate energy consumption related to
other processing parameters. These models are very useful when comparing different equipment’s and
working parameters.
In most manufacturing processes, the amount of energy consumed for the actual process is only a
fraction of the total amount of the total energy used. In those cases, a significant part of the energy
consumed during the process is due to the start-up and maintaining the machines idle, in the case of
injection moulding, this occurs mainly when using hydraulic or hybrid equipment, as seen previously.
17
Figure 3.3: Energy used in an automobile machining line as function of production rate [27]
The example of Figure 3.3 represents the amount of energy used in a machining line for the automobile
industry as function of production rate. In this case 85.2% of the energy consumed is used to maintain
the equipment in a ready state, by maintaining oil pressure and other parameters at ideal conditions.
According to this model the total energy consumption during the process can be formulated using a
fixed parcel as function of the equipment characteristics (machines size, power, parallel equipment, hot
runner etc…) and a variable parcel, as function of processing parameters [equation (5)].
𝐸𝑡𝑜𝑡𝑎𝑙 = 𝐸𝑓 + 𝐸𝑣 ( 5 )
Where 𝐸𝑓 is the fixed parcel of energy consumption, and 𝐸𝑣 is the variable part.
3.2.5. SEC vs Throughput
One of the most used SEC models is the SEC vs throughput, because the majority of variable
parameters (Shot size, clamping force, cycle time etc…) can be accounted in the quantity of material
going through the production line [28] [equation (6)].
{𝑆𝐸𝐶 =
𝑃𝑡𝑜𝑡𝑎𝑙
�̇�=
𝐸𝑡𝑜𝑡𝑎𝑙
𝑚=
𝑃𝑓
�̇�+ 𝑝𝑣
𝑃𝑣 = 𝑝𝑣 ∗ �̇� ( 6 )
18
Where 𝑃𝑡𝑜𝑡𝑎𝑙 is the total power required, 𝐸𝑡𝑜𝑡𝑎𝑙 is the total energy consumed, 𝑃𝑓 is the fixed power
consumption, 𝑃𝑣 is the variable power used, 𝑝𝑣 is the variable power per unit of mass, 𝑚 is the shot size
and �̇� is the machine throughput �̇� =𝑚
𝑡.
Figure 3.4 illustrates equation (, it shows that SEC consumption is reduced significantly with the increase
of throughput for hydraulic machines.
Figure 3.4: SEC vs Throughput [28]
3.2.6. Artificial Neural Networks
Artificial neural networks systems are widely used throughout the scientific community to model complex
problems found on several different industries [34], [37]. Some authors developed approaches based
on neural networks systems to model energy in different areas. Even though this approach is complex
to apply and requires large amount of data in its development, after the training stage they became fairly
simple to apply in the industry.
Artificial neural networks (ANN) are inspired by the way biological systems work. Much like people,
ANNs learn by example. An ANN is developed to solve a certain problem by a training phase, were the
network learns using examples from the data set available, [38]. Artificial neural networks resemble the
human brain in the following ways,[39]:
1. The knowledge of the subject to model and its variables is acquired by learning,
2. The obtained knowledge is stored in the connection between neurons called synaptic weights,
Artificial neural networks are particularly useful when modelling complex systems, given that they are
able to deal with non-linear problems, by learning from the data inputted, allowing the models to select
19
or discard data to achieve the pretended objective. To achieve this, neural network systems, are
developed in three stages, learning, validation and testing, [34][38].
ANNs are composed by three main groups of layer. The first layer is usually called the input layer, and
it is followed by a set of hidden layers, also called neurons. After flowing thru this layers the information
is then presented in the final layer, the output layer, [34]. The composition of a generic artificial neural
network in displayed in Figure 3.5.
Figure 3.5 Schematic of a multi-layer artificial neural network [34]
Neural networks are trained to learn the characteristics of the problem in hand by a process
called backpropagation. Backpropagation is a process in which the network is repeatedly submitted to
the input data set. During this process the training algorithm compares the output of the network with
the experimental results and computes de error, which is later back propagated to the neural network.
In each iteration the networks revaluates the weight of each input to reduce the error. This process is
commonly known as training the neural network, [39].
The applications of neural networks are extensive as they take advantage of their configuration to
simplified otherwise complex non-linear problems. Neural networks are widely used in the fields of sales
forecasting, industrial process control and costumer research amongst others. They especially useful
because there is no need to develop specific algorithms to solve each problem. Furthermore there is no
need to understand the computational procedure used. They are also particularly useful in real time
systems as the computational time is usually small, [38].
20
4. Methodology
The following chapter explains the methodology chosen to approach this thesis. The steps took are
shown and explained in detail in the next three sub-chapters. The first sub-chapter, preparation, explains
the preparation phase of the project. The second, experimental work, represents the gathering data
phase and the stay at the company. The last part, model development, treats the work done with the
selected models, using the data gathered in the company.
Figure 4.1- Scheme of the methodology chosen to approach the thesis
21
4.1. Literature Revision
The preparation stage of the project was the first to be done. Before going to the company and start
gathering data, there was the need to understand and decide what information was relevant to this
objective. This phase started before the stay at the chosen company in order to prepare the work to be
done and understand the relevant variables and conditions of the process in terms of energy
consumption.
Choosing the variables
The first group of information to define will be the variables with influence on the energy consumption.
This will be achieved by gathering information available on the literature, researching existent models
around energy consumption in the injection moulding industry and studying the injection process.
This selected group of variables will later be tested, to identify the relation between each one and the
energy consumption of the injection moulding process in different scenarios
Selecting relevant approaches
Once completed the phase of identifying the variables, a research process will be conducted to identify
and select relevant approaches to energy modelling in the industry of injection molding. This process
was achieved by researching several models, referring not only to the injection molding industry, but
also, to others processes, such as, machining. The chosen approaches will later be adapted and tested
with data from the experimental work.
4.2. Experimental work
This phase took place in the selected company. During this time, there was gathered information about
the process as well as data referring to energy consumptions.
Energy consumption measures
The company selected has a large number of injection machines (around 80) working in three shifts, 24
hours per day with several different moulds, meaning that it’s possible to gather massive quantities of
data providing that the right methodology is applied.
Two methods were used to measure energy consumptions, both are explained in the next chapter. One
of the equipment used to gather the measures of energy consumption involved an extensive
22
parametrization process, once it was new to the company and it hadn’t been properly tested. At the
same time as the measuring procedure took place, the information was gathered regarding processing
condition of the part, the machine and the mould.
During the period in the company the consumption of peripherals necessary to the injection process
were measured. Consumptions of material feeding systems, cooling systems and waste recycling
machines were gathered in order to understand the process as a whole, and justify possible differences
in the consumption of apparently identical situations.
Gathering information on the process
Taking advantage of the extensive knowledge of some of the professionals that deal with the process in
a daily base, the stay in the company involved a significant process of understanding the details of the
injection moulding industry. Factors such as the properties of the injection machines and their relation
with the age of such equipment were studied to explain possible differences in the consumptions of
similar machines.
4.3. Model development
This phase consists of the analysis of the data obtained in the prior stage of the project and its usage in
the selected approaches from the literature.
Data analysis
Once the experimental work was completed, there was a phase of organizing and analyzing the data
gathered. The first step was to do a detailed analysis of the instant power consumption graphics
obtained. In each one of these there was computed the average power consumption for the experience,
and in a few of them there were identified the different stages of the injection cycle.
The next step was to test the influence of the variables previously selected, to better understand the
characteristics of the process with the biggest contribution to the consumption. In this stage there were
created graphics relating parameters such as the cycle time and installed power of the machine, with
the energy consumption of the experimental cases.
The analysis of the data was then used simultaneously with the knowledge acquired during the time in
the company to find new variables in order to explain unexpected values in the measures.
23
Developing, Testing and adapting relevant models
The selected models were tested, to understand the applicability of each one in the experimental case
in hand. For this, the data gathered was used to estimate the consumption output with the selected and
errors were calculated to evaluate their precision.
A new approach was developed to further improve the modelling of the practical case study.
Comparing the different models
After developing, testing and adapting the three different models to the case study in hand, the
approaches were compared. This process led to creating a set of advantages for each model to specific
situations possible in the industry.
24
5. Energy consumption analysis
The following chapter explains both the methods of obtaining data and the first tests to the information
gathered. It is divided in three sub-chapters. The first one explains in detail both methods used to gather
data and the advantages of each one. The second shows the influence of different parameters on the
consumption of energy. The third and final sub-chapter contains information about the influence of some
properties of injection machines and their parameterization on the energy consumptions.
5.1. Measuring Equipment
The measurements were taken using two different equipment’s. Each equipment used is explained
bellow, and the method used for each one is in detail in the next sub-chapter.
Equipment A
The first equipment used to evaluate the energy consumption of injection moulding machines and
peripherals was a PROVA 6830 power and harmonics analyser, as shown in Figure 5.1.
This equipment allows the semi-continuous monitoring of active power, reactive power, tension, intensity
for the three phases plus the neutral as well as the monitoring of the angle between phases. It works in
a semi-continuous way because it allows the user to select the measurements interval which can range
from 2 to 6000 s/measurement.
Figure 5.1- Equipment used to measure energy consumptions, PROVA 6830
25
Equipment B
In an attempt to increase the volume of data gathered during the stay in the company a second
equipment was used.
The visited company recently acquired and installed a system capable of monitoring the energy
consumed by each machine in live time. This system is uses a set of clamps for each phase, and is
capable of measuring the following parameters:
1. Current
2. Voltage
3. Effective power
4. Energy
5. Angle between phases
This systems uses a Bus terminal manufactures by BECKHOFF (KL3403) to read the measured values
and an Ethernet TCP/IP bus coupler (BK9100) to send the data to the computer. This equipment is
capable of reading the parameters above 64000 times per second. It later calculates the true root mean
squared to eliminate the influence of the peaks in current and output the energy consumption to the
computer in 30 seconds intervals.
5.2. Measuring method
Equipment A
The data gathered in the development stage of this thesis
was obtained using two different methods. The first method,
using equipment A, requires a time consuming setup
procedure as it need to be repeated for each measurement.
To apply this method the first step is to connect the
equipment described in the previous sub chapter to the
injection machine electric box as shown in. This process
requires the presence of an experienced technician in order
to prevent accidents affecting both the machine and the
operators. The correct set up of the measuring equipment
was assured using clamp meters to compare the current
output.
Figure 5.2- Instalation of equipment A
26
As said before, the measuring equipment A, used in this method, provides a precision of up to a reading
in each two seconds, which translates to 30 readings per minute. To achieve the maximum precision
possible, each measurement was taken during an interval superior to 30 minutes, granting a total of, at
least, 900 readings. During the experiment duration the machine was monitored to insure that there was
no external influence in the process.
The data obtained was exported to the computer as a .CSV file, and then converted to excel. This
resulted in a file containing not only the active power during the experience, but also, other parameters,
such as the current and the tension in each phase, the angle between phases and reactive power. The
result of applying this method can be translated to a graphic as shown in Figure 5.3.
Figure 5.3- Example of the consumption profile obtained with method A
The graphic displayed in Figure 5.3 is an example of the measurements taken. By analyzing it closely,
is possible to identify a cyclic behavior that works as an indication of the injection cycles that are
occurring during the time of the experience, as illustrated in Figure 5.4. The graphic in Figure 5.3 was
obtained by monitoring the energy consumption of a 120 ton injection machine during a period of around
30 minutes (2000s).In Figure 5.4 it´s displayed a detailed view of an energy consumption graphic,
illustrating the different stages of the injection and it´s cyclic behaviour. This measurement was taken
from an injection machine with a capacity of 110ton, injection a part made of ABS with a cycle time of
29,3 s.
0
2
4
6
8
10
12
14
16
0 200 400 600 800 1000 1200 1400 1600 1800 2000
Ins
tan
t p
ow
er
(kW
)
Time (s)
Power consumption
Média…
27
Figure 5.4- Detailed view of a power consumption graphic (Method A)
In Figure 5.5, it´s shown a detail of the previous image, in this it´s possible to identify the different stages
of the injection cycle, the opening and closing of the mould, followed by the injection and at the end the
cooling of the injected part.
Figure 5.5- Detailed view of the different stages of the injection cycle
Equipment B
The second method used to measure energy consumptions takes advantage of the energy monitoring
system installed on the company visited. Using this system, engineers at the company can monitor live
information about the energy used by each individual injection machine and cross it with the information
provided by the production management software also available.
0
10
20
30
40
50
60
70
0 20 40 60 80 100 120 140 160 180 200
Ins
tan
t p
ow
er
(kW
)
Time (s)
Power consumption
0
10
20
30
40
50
60
70
20 30 40 50 60 70 80 90
Ins
tan
t p
ow
er
(kW
)
Time (s)
Power consumption
2
3
1- Opening and closing of the mould;2- Injection;3- Packing and cooling;
1
28
This method doesn´t require any pre-measurements set up, once it was all done in the beginning of the
data gathering. The fact that it doesn´t require any preparation, means that there was no need to wait
for the technician as in the previous method, thus making it possible to acquire a larger number of
measurements.
As explained before, the used equipment measures the power consumption of the machines in small
time frames, around 64000 samples/s. It calculates the root mean square of those values and sends the
information to the computer in 30s intervals. This facts means that it is impossible to use the graphic
resultant of this equipment to identify the different phases in each cycle. Figure 5.6, shows an example
of the graphics obtained using this method. As seen in the image, the graphic receives a new impute
every 30 seconds, and that value is a result of a series of measurements taken each second, granting
a semi-continuously monitoring of the power usage.
Figure 5.6- Example of power usage graphic obtained by method B
In order to by-pass possible errors in communication between the system and the measuring
equipment’s, we chosen a measuring interval no shorter than 2 hours, like the one shown in Figure 5.6.
The graphic shown was used in combination with the production management software to assure that
during the time of the experience, the machines were working continuously. The power consumption
during the time of the experiences was gathered by exporting the values to and excel file and analysing
it. Using this equipment it was possible to gather around 200 different measurements, even with the loss
of a significant amount (around 90) due to a set-up error.
The utilization of two different equipment allowed for the comparison and validation of, not only the
equipments used, but also the method and the set up procedure. This led to finding problems in the
29
initial configuration of the system installed in the company, and was key to the correct parametrization
of the same system.
5.3. Experimental data
The experimental data was gathered during a period of approximately 2 months in the visited company.
During this time, both equipment’s described previously were used in order to collect a total of 200
measurements of energy consumption, each associated with processing, material and machine
properties related to the injected parts in study.
The energy consumptions were measured in a vast array of injection machines. Ranging from 35 to 830
ton of clamping force, as shown inTable 5-1. A complete list of the machines is available in annex II.
Clamping force [ton] Number of machines
[35-100] 27
]100-200] 17
]200-300] 6
]300-400] 5
]400-500] 0
>500 12
Table 5-1- Distribution of the different machines by clamping force
The data gathered includes energy consumption measurements of different parts made of four different
materials, ABS, PS, PP and POM. In order to model the energy consumption of the machines used in
the experience, generic material properties were chosen. The properties used for the different materials
used in the visited company are displayed in Table 5-2.
30
Material ABS PS PP POM
𝝆 [𝒈
𝒄𝒎𝟑] 1,06 1,05 0,901 1,41
𝑪𝑷[𝒌𝑱
𝑲𝒈º𝑪]
1,67 5,02 1,92 1,48
𝑯𝒇[𝑱
𝒈]
0 0 0 326
𝑻𝒎𝒆𝒍𝒕[º𝑪] 233 214 200 1900,7
𝝀 0 0 0,75 0,7
Table 5-2- Material properties used for modelling energy consumptions [40]
The energy consumption were measured in the different machines available in the company. In some
cases the energy consumption was measured multiple time in the same machine, a few of those
maintaining the same injected part and the others comparing different parts, this method of obtaining
data allow the posterior application of different types of energy modelling approaches .
In Table 5-3 is exemplified some of the data gathered during the measurements, with the type of
information collected for each sample. It´s possible to see that, in general, the power consumption
increases with the increase of the machines installed power.
Machine Clamping
force [ton]
Installed
power
[kW]
Cycle
time [s]
Injected
mass [g]
Material Experimental power
consumption [kW]
63 140 34,5 27,3 106,2 PP 9,17
40 135 35 41,8 41,8 PS 9,91
46 50 21 16,1 8,51 ABS 5,25
92 260 60 44,9 104 ABS 13,75
58 350 71 51,2 154,08 PP 12,39
56 80 24,5 21,7 14,67 ABS 6,38
Table 5-3- Example of the data gathered
31
5.4. Data analysis
As said before, the energy consumption of injection machines can be influenced by a number of factors.
The ones chosen as the key parameters with influence on energy consumption were material type,
injected mass, injection time, installed power and thickness. After presenting examples of the data
gathered, in the present chapter there will be shown the relation between energy consumptions and the
mentioned variables.
The machines installed power is expected to be the main factor with influence on consumptions once it
translates the dimension of the machines, and thus, the dimension of the injected part. The mentioned
relation can be observed in Figure 5.7. Although at first sight the graphic doesn’t appear to show any
relation between this to parameters, by analyzing it closely, it is possible to see that the consumption
increases for higher capacity machines.
Figure 5.7- Influence of the machines installed power on the energy consumption
The main problem with the graphic displayed in Figure 5.7 is the large concentration of data for low
power and low capacity machines. In these cases there seem to be other factors with more influence
than the installed power of the machine. This evidence will be studied in the next chapters.
Figure 5.8 shows a close up of the previous graphic (Figure 5.7), demonstrating the issue with having a
high concentration of data for similar machines. In both figures there are marked the different materials
injected for each measurement, it´s is possible to see that there is no clear relation between the material
and neither of the variables shown.
0
500
1000
1500
2000
2500
3000
3500
0 50 100 150 200 250Energ
y C
onsum
ption p
er
Cycle
[kJ]
Installed Power [kW]
Energy consumption vs installed power
PP
POM
PS
ABS
32
Figure 5.8- Close up of the influence of the machines installed power on the energy consumption
Is expected that the influence of the cycle time in the energy consumption of the process shows the
same behaviour as the installed power, that is, for longer cycle times there should be higher power
consumption. Figure 5.9 shows the influence caused by the increase of the cycle duration on the energy
consumption. The graphic displays the expected behaviour with the exception of a few cases where the
consumption appears to be higher than expected. Once again there is no clear influence of the Material
used in each measurement in the energy consumption.
Figure 5.9- Influence of the cycle time on the energy consumption
0
500
1000
1500
2000
2500
3000
3500
0 5 10 15 20 25 30 35 40 45 50Energ
y C
onsum
ption p
er
Cycle
[kJ]
Installed Power [kW]
Energy consumption vs installed power
PP
POM
PS
ABS
0
500
1000
1500
2000
2500
3000
3500
0 10 20 30 40 50 60 70 80 90Energ
y C
onsum
ption p
er
Cycle
[kJ]
Cycle Time
Energy consumption vs cycle time
PP
POM
PS
ABS
33
The relation between the mass of the injected material with the energy spent on the process appears to
display the same problem that the one seen on the Energy consumption vs Installed power graphic.
There appears to be a large concentration of data for machines injecting low quantities of material
(Figure 5.10) in which the variation of results within the same amount of material is higher than the one
seen between different injected masses. This problem is translated in the apparently non-linear growth
of the graphic displayed in Figure 5.10.
Figure 5.10- Influence of the injected mass on the energy consumption
Figure 5.11 however show a detailed view of the graphic shown before. Although is possible to identify
the problem with the large variation within the same range of injected mass is also possible to see that
the values of energy consumption are increasing with the increase of the mass of injected material, as
expected. Once again it is impossible to identify any relation of the parameters shown in the graphic
with the material used.
0
500
1000
1500
2000
2500
3000
3500
0 500 1000 1500 2000 2500 3000
Energ
y C
onsum
ption p
er
Cycle
[kJ]
Injected mass
Energy consumption vs injected mass
PP
POM
PS
ABS
34
Figure 5.11- Detail of the influence of the injected mass on the energy consumption
The last variable with expected influence on the power used to injected plastics is the thickness of the
final part. The premises of this assumption are that thicker areas of an injected part take longer to cool
down, which is translated in longer cooling times of the process and thus more extended cycle times.
As seen before, higher cycle times are expected to cause higher energy consumption values. However,
as seen in Figure 5.12, the graphic does not behave as expected, and the thickness of the injected parts
does not appear to have any direct influence on the energy consumption. The reasons behind this
observation may have to do with the complexity of the part. The premise that thicker parts need higher
cooling times is true for simple geometries, however, that might not be case for parts with high geometry
complexity. In this case the mould used can have areas where the refrigeration is more difficult, leading
to higher cooling times for parts with low thickness aspect.
Figure 5.12- Relation between the energy consumption and the maximum thickness of the injected part
0
100
200
300
400
500
600
700
800
900
1000
0 20 40 60 80 100 120 140 160 180 200
Energ
y C
onsum
ption p
er
Cycle
[kJ]
Injected mass
Energy consumption vs injected mass
PP
POM
PS
ABS
0
500
1000
1500
2000
2500
3000
3500
0 5 10 15 20Energ
y C
onsum
ption p
er
Cycle
[kJ]
s [mm]
Energy consumption vs Maximum thickness
PP
POM
PS
ABS
35
5.5. Causes of the variation of energy
consumptions for similar processes
During the data gathering stage there was a significant amount of contact with people from the industry,
some of which works closely with plastic injection machines, both at the ground floor level and at the
office level. During this time were identified a number of factors that can influence the energy
consumption, outside of the ones presented before. This factors are mainly related to characteristics of
the machines used.
The variation of energy consumptions for processes in similar conditions can be due to different reasons:
1. Parametrization of the process
2. Technology of the machine
3. Part and material properties
In most injection molding factories, the parameterization of the machines for each mold used is part of
the responsibility of different teams, each has to setup a group of machines, making sure that the final
product meets the standards required by the costumer. During the setup operations there is some
flexibility in the parameters chosen. These parametrization in setup is also linked with the productivity
required by the company. Some companies have their production more focused on reducing cycle times
to increase productivity while others prefer to reduce the cycle time to increase the quality of the injected
parts. This two different approaches have distinct effects of the power required to inject a certain part.
Productivity focused parameterizations, while reducing cycle times, increase the temperature of the
material, the pressure used and often involve the reduction of the refrigerator fluid temperature. This
usually causes a higher energy consumption. This factor can explain the different values of energy
consumed in apparently similar processes in terms of cycle time, machine and part.
The second point, technology of the machine was explained briefly in chapter 2. There are three different
types of injection machines. Electric, hybrid and hydraulic. Is expected that the hydraulic machines have
the higher energy consumption of the three types. That is due to the fact that hydraulic machines require
the motors driving the pumps to be continuously working in order to maintain the oil pressure and other
working conditions. This fact is translated in the generation of an idle energy consumption, this means
that even when the machines are not performing any action there is still power consumption. This
evidence is illustrated in Figure 5.3, where it´s possible to see that at any point in the experience the
instant power is null.
Electric machines, on the other hand, do not require motors to drive pumps. They simply use electric
motors to move the necessary components, thus, when the machine is stopped in a position, the motors
36
can be shut down, not consuming energy. Because of this factor, electric machines are expected to
consume around half of the energy than hydraulic machines.
It is also expected for newer machines to consume less energy than old ones. Recent machines are not
only more efficient due to new motor technologies, advanced process parametrization control but also
due to the presence of power saving features, such as variable-frequency drives. The motors used to
power the pumps of the hydraulic injection machines are usually three phasic with a fixed speed. This
means that independently of the solicitation caused by the process the motors work at their full capacity.
There is, however, a system to control the rotation of the electric motors. This system is called Variable-
Frequency Drives (VFD). This drive is present is most of the recent injection machines, and can be
installed in most three-phase induction motors.
VFD manufacturers claims that the energy savings achieved by using this equipment’s can go up to
50% of the total energy consumed during the process.
To test this claim, several tests were conducted in which the energy consumption was measured in the
same machine, with equal working conditions, but with the variable-frequency drive turned on and off.
Figure 5.13 represents the comparison between measurements took from machine 58 in a period of 20
minutes with the VFD turned on and off.
Figure 5.13- Power consumption profile for machine 58 with and without the VFD
By analyzing the graphic it is possible to see that the average power consumption, when using the
variable frequency drive is 18.3 kW. Machine 58 is an injection machine with a clamping force of 350
ton, and 71 kW of installed power.
0
10000
20000
30000
40000
50000
60000
70000
0 200 400 600 800 1000 1200
Ins
tan
t p
ow
er
(Wa
tt)
Time (s)
VFD_on
AveragePowerVFD_ON
VFD_off
AveragepowerVFD_off
37
In the graphic displayed in Figure 5.13 there is also representation of a similar experiment, taken with
the installed VFD temporarily turned off. This experiment allows us to see the total energy saving
achieved by this device. In this case, the machine was operating with the same parameterization,
injecting the same part without using the aftermarket VFD installed. By analysing the image it´s possible
to see that in this case the average power consumption of the machine is superior, 28.5 kW. This values
mean that with the VFD turned on, the consumption of this machine is around 64.21% of the standard
consumption, this represents an energy saving of around 35.79%.
The same methodology was applied to other machines, the results are shown in Table 5-4.
Machine Installed power
kW
Consumption (VFD off)
kW
Consumption (VFD on)
kW
Power saving
%
31 45 11,8 8,7 26,3
32 45 18,9 11,7 38,2
58 71 28,5 18,3 35,8
110 108 39 31,4 19,5
Table 5-4- Comparison of the average power consumption between the same machine, using VFD's and working in normal condition.
Looking at Table 5-4, it is possible to see the differences in energy consumption caused by the usage
of VFDs. It is evident that in the studied cases, systems like this have a considerable impact. For the
four cases analysed in this company, the variable-frequency drive provides an average power
consumption saving of around 30%. This value, although lower than the 50% claimed by some
manufacturers, represents a significant reduction in power consumption, and therefore it is a factor to
consider when evaluating this parameter.
38
6. Model Development
The following chapter displays the tests done with the selected models and explains in detail how they
were conducted. It is divided in three sub-chapters. The first, specific energy consumption model,
explains how SEC models were applied and shows the results of using them. The second, process-
based model, works like the previous but using the selected PBM type model. The third and last sub-
chapter, neural network explains the development of the model, and shows the results achieved.
6.1. Specific Energy Consumption
The applications of specific energy consumption models, SEC, were explained in chapter 2. This
approach to model energy consumption is the most widely used, mainly because it can be done simply
and can provide useful estimations.
The first step in applying SEC models to the data gathered is to develop a graphic relating the specific
energy consumption from the different measures with the throughput. Figure 6.1 shows the evolution of
the specific energy consumption, with the throughput. The graphic behaves as expected, showing that
for lower values of work flow the SEC value tends to the infinite and when the throughput increases the
energy becomes closer to zero. The data displayed on Figure 6.1 was calculated equation (7).
𝑆𝐸𝐶 =𝑃
𝑚 ̇=
𝐸
𝑚 ( 7 )
Figure 6.1- Graphic showing the relation between the specific energy consumption of the measures took and the throughput for each case.
39
In the above figure there’s a magnification of the area of the graphic with the biggest concentration of
experiences to better understand the behaviour of this variable. In this graphic it’s possible to see that
for the main part, the specific energy values follow the tendency line, with only a few clearly higher than
expected.
It was explained previously in chapter 1, that SEC models are usually developed using the average
specific energy consumption for a series of experiments. This approach was tested by Thiriez and
Gutwoski [3], in an experiment where they calculated the expected SEC value for a series of energy
measures. The average SEC value proposed by Thiriez and Gutwoski was 11.3 MJ/kg. The average
SEC value calculated using the data gathered in the company is 12.2 MJ/kg. The difference in both
estimations can be explained by a number of reasons, the first being the difference in the production
planning technique of the companies. As explained before, some companies have a focus on higher
production rates and therefor set their operating parameters to achieve that goal, while others choose
to reduce the quantity of parts produced to improve the quality of the final product. The difference in the
technology of the machines used can also be a factor, as seen before newer machines include systems
like VFDs that can cause a significant reduction in energy consumptions. The differences in the material
and geometry of the injected part may have a dramatic effect in the specific energy consumption.
Looking at the results of calculating the SEC value for each experiment it is evident that there are
significant variations, however, the average value obtained is still fairly close to the ones found in
previous works [3], [15]. This leads to the conclusion that a simple SEC vs throughput analysis is a good
approach to modelling energy consumptions.
In an attempt to further improve the precision of the SEC methodology, new approaches were tested.
The first being the analysis in terms of clamping force, and the second separating the different materials
used.
6.1.1. SEC vs Material type
The large amount of data gathered at the company, means that it is possible to select considerable
quantities of data for each material used. This fact allows the usage of new approaches to model the
energy consumption using SEC models. The first tested approach was to compare the specific energy
consumption of each process with the material used in the correspondent part. For this effect, the same
graphic as before was plotted, however, this time with the used materials highlighted in different colours
(Figure 6.2).
40
Figure 6.2- Graphic showing the relation between the specific energy consumption of the measures took and the throughput for each case, highlighting the different materials used.
In the above figure there’s a magnification of the area of the graphic with the biggest concentration of
the experiences to better understand the behaviour of this variable. In this graphic, similarly with what
has been said before, the different materials used are highlighted in different colours. Looking at it
closely, it doesn’t appear to be possible to distinguish different tendency lines for each material .To
further analyse this evidence, the tendency line for each of the materials were traced. The result, as
shown in Table 6-1, is that there is no significant increase in the correlation obtained when compared to
the previous analysis (0,78).
Material R-squared (𝒓𝟐)
POM 0,64
PS 0,74
PP 0,77
ABS 0,87
Table 6-1- Correlation of the curve relating each material specific energy consumption and its throughput
41
The table above leads to the exclusion of this approach. By comparison with the previous approach,
SEC vs throughput, this distribution by material type does not appear to increase the precision of the
model, thus the previous approach is consider to be superior to the SEC vs material type.
6.1.2. SEC vs Clamping Force
The company visited in the data gathering stage has a large number of machines ranging from low
clamping capabilities to high capacity of clamping force. There are several measures available for each
machine. That factor combined with the fact that there are several machines for each class of clamping
force, means that it is possible to calculate average SEC values for practically each one of the clamping
forces available at the visited company. This method, such as the previous, works as an alternative to
computing average SEC values for the totality of the data, allowing the user to reduce the influence of
the injected part in the estimation.
Figure 6.3- Graphic relating the specific energy consumption of the different experimental cases with the correspondent machines clamping force. The average SEC value for each case is highlighted.
The analysis of Figure 6.3 shows that the average SEC value, in general, decreases with the increase
in the machines clamping force. There are, however, exceptions in a few cases.
The results shown above, appear to have some differences to what was expected. As said before, it is
expected that the specific energy consumption decreases with the increase in clamping force. That is,
because, a bigger injection machine is able to inject more material, thus having a higher throughput.
While this is true, a bigger machine doesn’t necessarily consume more energy in a proportional way to
a smaller one.
0
20
40
60
80
100
120
SE
C [
MJ/k
g]
Clamping force [ton]
SEC vs Clamping forceSEC
Average SEC
35 40 50 60 80 85 100 110 130 140 170 180 200 210 220 240 250 260 280 300 320 380 650 830
42
Looking at the graphic it’s possible to see that, especially for smaller machines, the standard deviation
is considerably big. In some cases, such as the 50 and the 80 ton machines, it’s obvious that the average
specific energy consumption is greatly affected by one or two high values. The same can be said of the
650 ton case, where there is a concentration of small SEC values and one considerably higher, resulting
in an increase in the average value. Given that the specific energy consumption of a process is the
result of the energy spent by unit of mass used, is apparent that the high values of SEC described, are
the result of a significant decrease in the mass of the respective injected parts.
To exemplify the fact mentioned above, Table 6-2 contains every measurement took in a 50ton machine.
As shown in Figure 6.3, a large concentration of the data available was gathered in machines with 50ton
of clamping force, mainly because that’s the most common size of machine available in the company.
Machine Installed
power
[kW]
Injected
mass [g]
SEC
[MJ/kg]
Machine Installed
power
[kW]
Injected
mass [g]
SEC
[MJ/kg]
42 20 5,56 24,56 46 21 18,75 6,11
54 21 10,39 29,01 53 21 15,13 21,18
76 21 20,1 4,18 54 21 6,57 104,29
54 21 13,25 38,62 46 21 49,61 2,94
42 20 7,46 15,36 53 21 16,4 19,94
76 21 14,5 9,25 80 21 5,55 48,11
76 21 18,72 7,44 76 21 5,04 23,05
46 21 8,54 9,93 80 21 11 9,37
53 21 40,9 10,33 76 21 12,5 11,94
53 21 40,9 9,86 54 21 20,6 17,55
Table 6-2- Average values of specific energy consumption [MJ/kg], sorted by clamping force [ton]
The table above, shows that the SEC values for the same machine, can display large variations
depending on the injected part as well as the processing condition.
43
Using the values presented in Table 6-2 it was calculated the average SEC value for the 50ton machines.
It is expected that a machine of this capacity has the specific consumption of 21.15 MJ/kg. This value,
as expected is higher than the one computed in the first approach, using a simple SEC vs throughput
for the totality of data. It is expected that smaller machines display higher SEC values that larger
machines, that’s because the decrease in throughput present when using smaller machines is bigger
than the reduction in energy consumed during the process.
Table 6-3 shows de value of average SEC for the classes of clamping force with more data. The rest of
the machines were excluded, because the lack of data (less than five measurements) available doesn’t
allow us to obtain proper estimations of the average SEC.
Clamping force [ton] Average SEC [MJ/kg]
35 38,26
50 21,15
60 15,28
80 29,71
85 7,80
100 8,47
110 4,21
130 8,54
140 4,40
170 8,37
Table 6-3- Average values of specific energy consumption [MJ/kg], sorted by clamping force [ton]
44
6.1.3. Combined Data
A final analysis was made to verify the capabilities of SEC models, when used to estimate energy
consumptions between different companies. For this effect, the previously presented SEC models were
tested using data from other similar studies [12], [15].
Company Measures
A 180
B 29
C 11
Total 220
Table 6-4- Distribution of the data used
This analysis allows us to access not only the capabilities of SEC models, when used in a multi company
industrial environment, but also to verify the weight of the differences between the different companies.
It is expected that there are significant changes in SEC values between the different companies due to
the big amount of differences that can be found in this industry:
1. Machines used
2. Injected parts geometry
3. Parametrization of the machines
4. Machine/Part fitness
The first tested approach was to use the combined data in a SEC vs throughput analysis. The results
are displayed in Figure 6.4.Table 6-5
45
Figure 6.4- Graphic showing the SEC vs Throughput for the data from companies A, B, C
In the graphic shown above, it’s evident that the data from company C, [15], is situated in the top part
of the spectre. Opposite to that, the data from company A, [12], is at the lower limit of the SEC values
displayed. The average SEC value for the combined data is 11.32, identical to the one defined by
Gutwoski, 11.3.
The second approach tried for this data set was to evaluate the SEC values divided by the different
machines available. For this objective the measures were grouped by the machines clamping force. The
result of this analysis is displayed in Figure 6.5.
46
Figure 6.5- Table showing the specific energy consumption for the measures taken in the three companies group by the machines clamping force
The results don’t show considerable differences when compared to the ones obtained using exclusively
data from company A. There are however some conclusions to be taken of this analysis:
1. There appears to be insufficient data gathered in company c to do this analysis, however, the
few measures used are within the range found in company A
2. The data from Company B blends in the one gathered in company A.
Based on the evidences found on the graphic is possible to conclude that an analysis in terms of SEC
vs clamping force, can be used for different companies.
The last analysis made with the present data set, was a SEC vs Material type. This analysis however is
inconclusive given that the inclusion of the data gathered in previous works includes new materials and
does not add a significant amount of data for the materials found in the visited company.
6.2. Process-Based Model
One of the approaches tested was to use a process-based model (PBM) to estimate energy
consumptions. As explained before, this type of models intends to estimate energy consumptions by
taking into consideration the combination of the main factors involved in the process. The model to be
used in this approach is the Ribeiro et al [12].
0
20
40
60
80
100
120
0 100 200 300 400 500 600 700 800 900
SE
C
Clamping force
SEC vs Clamping force
Company A
Company B
Company C
47
The model used, as any other, uses a series of inputs to predict a certain output. In this case, the output
is the energy required to produce a certain part. The inputs, however, are not so simple. This model
uses a few different inputs in order to provide a close approximation to the process, hence the name
process-based model.
The inputs necessary to use this PBM are the following:
Inputs Outputs
Machine type (electric vs hydraulic)
Installed power
Part geometry
Cycle time
Injected mass
Material type
Power consumption
Table 6-5- Inputs and outputs used in Ribeiro et al model [12].
The selected process-based model was previously tested in two different companies. The first was
during its developing stage, and the second by Domingues [15]. The original model, used the inputs
shown above in Table 6-5, plus the maximum thickness of the part, given that it is expected to have a
direct influence on the cooling time, and therefor on the cycle duration. However, in his work, Domingues
[15], came to the conclusion that the maximum thickness of the injected part has no direct influence on
the energy consumption, result that is coherent with the data analysis done in this thesis.
The proposed model, it’s a dual model, meaning that it consists of two different parts, each approaching
a part of the process, in an attempt to simplify it.
1. Thermodynamic model
2. Machine model
𝐸𝑡𝑜𝑡𝑎𝑙 = 𝐸𝑡ℎ𝑒𝑟𝑚𝑜 + 𝐸𝑚𝑎𝑐ℎ𝑖𝑛𝑒 ( 8 )
The first parcel, is based on the thermodynamic models developed by a number of authors, [28], [33]. It
is based on the fact that a significant part of the power required in this process, is used to melt the
48
polymer and to fill the cavity of the mould. The formulation proposed by the authors is the one present
in chapter 3.2.1.
The machine part of the model, accounts for the influence caused by the different properties of the
machine used in the energy consumption, as well as a few different processing conditions. Therefore, it
is based on the usage of three coefficients.
𝐸𝑚𝑎𝑐ℎ𝑖𝑛𝑒 = 𝐶𝑓𝑀. (𝐶𝑓𝑃. 𝑃𝑖𝑛𝑠𝑡).𝑡𝑐
𝐶𝑓𝑇 ( 9 )
Where, 𝐶𝑓𝑀 is the machine type coefficient, 𝐶𝑓𝑃 is the power coefficient, 𝐶𝑓𝑇 is the thickness coefficient,
𝑃𝑖𝑛𝑠𝑡 is the power of the machine and 𝑡𝑐 is the cycle time.
As seen previously, hydraulic machines have high stand-by energy consumption, in fact, studies shows
that, in general, hydraulic machines consume 50% more energy than their electric equivalent [27]. Based
on this information, and on a comparison made by the authors, the machine type coefficient was defined
as 𝐶𝑓𝑀 = 0.5 for electric machines, and 𝐶𝑓𝑀 = 1 for hydraulic machines.
The machine power coefficient accounts for the impact of the machine’s installed power and its relation
with the part’s design. Precise formulation of this coefficient is nearly impossible, therefor, the authors
made a simplification based on the industrial data gathered. According to [12] the fitness of the machine
installed power to the part’s design is preponderant in terms of energy consumptions. To evaluate briefly
the machine fitness to the part, the authors use the ratio between𝑃𝑡ℎ𝑒𝑟𝑚𝑜/𝑃𝑖𝑛𝑠𝑡, meaning that for smaller
values there is an excessive machine dimension for the injected part characteristics.
To address the machine power coefficient, 𝐶𝑓𝑃, the authors considered that it is a measure of the
amount of the machines installed power that is actually used in the injection. In this case the 𝐶𝑓𝑃 is
computed by assuming that the measured energy consumptions must be equal to the ones estimated
by the model and then evaluating his variation for the different measurements [equation (10)].
𝐶𝑓𝑃𝑒𝑠𝑡 =𝑃𝑡ℎ𝑒𝑟𝑚𝑜
𝑃𝑖𝑛𝑠𝑡1.507 + 0.084 ( 10 )
Where the coefficients added to the 𝑃𝑡ℎ𝑒𝑟𝑚𝑜
𝑃𝑖𝑛𝑠𝑡 ratio are function of the variation observed.
The part thickness coefficient, 𝐶𝑓𝑇, considers the effects of the thickness in the machine energy
consumption for a specific cycle. This coefficient considers only the feature with higher thickness in the
part and was computed in the same way as the machine power coefficient. [Equation (11)]
49
𝐶𝑓𝑇𝑒𝑠𝑡 = 0.0884𝑠 + 0.7629 ( 11 )
Following an approach similar to the one used by Domingues [15], the model will be tested first in its
original configuration, and after that analysis, there will be selected two sets of data in order to
recalculate the coefficients and to test the final result. For this initial analysis, the thickness coefficient
won’t be used, given that it doesn’t appear to have direct influence on the consumption.
The error of the model predictions was calculated using the following equation:
𝑒𝑟𝑟𝑜𝑟(%) =𝐸𝑒𝑥𝑝−𝐸𝑒𝑠𝑡
𝐸𝑒𝑥𝑝 ( 12 )
Where, 𝐸𝑒𝑥𝑝 is the experimental value obtained in the company, 𝐸𝑒𝑠𝑡 is the model estimated energy
consumption. Table 6-6, shows an example of the error obtained for a group of 22 experiments.
Nº E_exp
[kJ]
E_est
[kJ]
Error
[%]
Nº E_exp
[kJ]
E_est
[kJ]
Error
[%]
1 180,8 212,61 -17,59 12 455,97 123,95 72,82
2 113,17 26,51 76,58 13 603,97 736,06 -21,87
3 151,72 80,3 47,08 14 245,75 213,11 13,28
4 84 63,82 24,02 15 284,08 155,88 45,13
5 214,96 89,47 58,38 16 118,8 46,77 60,38
6 202,31 308,04 -52,26 17 330,69 137,09 58,54
7 852,31 418,46 50,90 18 251,02 332,31 -32,39
8 194,4 107,04 44,94 19 617,38 386,77 37,35
9 176,63 145,34 17,71 20 136,54 37,46 72,56
10 397,76 178,33 55,17 21 344,78 449,77 -30,45
11 225,55 82,14 63,58 22 1719,67 323,08 81,21
Table 6-6- Example of the average error obtained using the selected PBM model
50
Analysing Table 6-6, it is possible to see that the error of the estimation varies significantly. The lines in
the table highlighted in grey represent examples of cases in which the estimated value is superior to the
real consumption. Given this results, it’s obvious that not only the variance of the error is considerable,
but also, the model estimates higher consumptions for some cases, and lower for others. This fact leads
to the conclusion that the proposed model is incapable to properly model the experimental data
obtained.
Using the model as proposed by its authors, resulted in errors superior to what was expected based on
the works done before. This can be due to a number of reasons, some of which are highlighted bellow:
1. The model was originally developed using a rather small amount of experimental data (11) when
compared to the number of measures available (180). This can create differences in the
adaptation of the coefficients for both companies.
2. The parts injected in the company visited are completely different from the ones used in the
original company.
3. The machines present in both companies are from different manufacturers, and differ in their
age.
4. The influence of the relevant parameters can be different in both companies, as seen for the
maximum thickness.
5. The injection parameters chosen by both companies can also be a factor. As seen before the
parameterization of the process be orientated towards a shorter cycle time or towards longer
time cycles (usually improved part quality).
6.2.1. New coefficients
In an attempt to adapt the Ribeiro et al. [12] model to the available data, an approach consisting of
recalculating the coefficients is going to be made. This approach was tried with success by Domingues
[15], who managed to recalculate new coefficients and validate those obtaining low values of error.
In order to calculate new coefficients for the original model, the data available from the experimental
stage was divided into two groups:
1. Development group (150)
2. Test group (30)
51
As explained before, the coefficients are calculated by linear regression. In this case, the thickness
coefficient will be ignored, as this parameter appears to have no direct influence on the energy
consumption. Therefore, the only coefficient to be calculated is the machine power coefficient, 𝐶𝑓𝑃. This
coefficient is calculated by doing a linear regression of the graphic comparing the computational
machine power coefficient with the relation between the thermodynamic and the installed power.
The computational machine power coefficient is calculated using the values of energy measured for
each experiment. Using equation13 it is possible to calculate this value.
𝐶𝑓𝑃𝑐𝑜𝑚𝑝 =𝐸𝑒𝑥𝑝−
𝐸𝑓𝑖𝑙𝑙+𝐸𝑚𝑒𝑙𝑡
𝜀𝑚𝑒𝑙𝑡,𝑓𝑖𝑙𝑙
𝑃𝑖𝑛𝑠𝑡𝑡𝑐𝐶𝑓𝑀 ( 13 )
Where 𝐶𝑓𝑃𝑐𝑜𝑚𝑝 is the computational machine power coefficient, 𝐸𝑒𝑥𝑝 is the experimental energy
consumption, 𝐸𝑓𝑖𝑙𝑙 is the energy needed to fill the cavity, 𝐸𝑚𝑒𝑙𝑡 is the energy required to melt the polymer,
𝜀𝑚𝑒𝑙𝑡,𝑓𝑖𝑙𝑙 is the thermodynamic efficiency of the process, 𝑃𝑖𝑛𝑠𝑡 is the installed power of the machine, 𝑡𝑐 is
the cycle time and 𝐶𝑓𝑀 is the machine type coefficient.
In Figure 6.6 it is shown the evolution of the machine power coefficient with the variation of the ratio
between the thermal and the installed power.
Figure 6.6- Evolution of the estimated machine power coefficient with the ratio between the thermodynamic power and the installed power.
Analysing the graphic is possible to see that there is no apparent relation between the variables. The
estimated power coefficient doesn’t appear to vary linearly with the ratio. This fact is supported by the
correlation factor, 𝑟2, being very low, 0.1184. Looking at the results, the conclusion is that there is
impossible to compute a new coefficient for the set of data used.
y = -2,1627x + 0,4325R² = 0,1184
0
0,2
0,4
0,6
0,8
1
1,2
1,4
1,6
1,8
0 0,05 0,1 0,15 0,2 0,25
CfP
𝑃_𝑡ℎ𝑒𝑟𝑚𝑜/𝑃_𝑖𝑛𝑠𝑡
52
In addition to the previous fact, the estimated power coefficient decreases with the increase of the ratio,
unlike what was expected. This result appears for the first time for this experimental case, being
completely different than what was seen in previous works. This behaviour leads us to believe that there
is an incorrect fitting of the machines used for each injected part.
In addition to the experiment made, and keeping the coherency with the analysis done previously, the
model was tested with data from the three companies. This, as expected by the previous results,
conducted to same conclusion. The model appears to be incapable of modelling energy consumptions
between different companies.
6.3. Neural Networks
The final approach selected to model energy consumptions an artificial neural networks (ANN) model.
As said before, this type of model is already used for estimating energy consumptions, and according
to Kalogirou, SA [34] it can be very useful in this area.
In order to model energy consumptions using artificial neural networks, a set of inputs had to be selected.
Being the output of such model the energy consumption, the inputs were selected based on the
information gathered from the literature and the data testing done to the experimental data. Of the
parameters selected initially as the main factors with influence on the energy consumption, only one
was excluded, given that, based on the experimental data, it appears to be unrelated with the
consumption, the maximum thickness of the injected part.
The selected inputs were the following:
Inputs Outputs
Installed power
Cycle time
Injected mass
Material type
Power consumption
Table 6-7- Inputs and output used in the ANN learning process
53
The database used in the development stage of this model is composed by 177 measures of energy, all
taken from the same company, in the data gathering stage of the thesis.
The total amount of data was divided into the three phases of the ANN developing, training, testing and
validation. To optimize the network, the proportion of the data used for each stage has to be selected
and tested. However, this is not the only parameter that is subject to variation during the development.
The following parameters will be tested:
1. Proportion of data that goes into, training, testing and validation,
2. Number of neurons in the hidden layer,
3. Training algorithm used
The number of neurons present in the hidden layer of the NN has influence on the results obtained,
therefore, this parameter has to be tested to achieve good results.
Another parameter that is usually taken into account when developing neural networks models is the
computational time. ANN’s, due to the considerable amount of data it requires, usually take long time to
compute, therefore this is taken into account when selecting the training algorithm and its respective
sets of data. In this case, however, the computational time required to train, test and validate the NN is
not too long and it doesn’t vary much between the different algorithms, mainly because the set of data
used, although large enough for this model, is not consider to be a big set of data for this sort of networks.
The training algorithm is key to the behaviour of the model. The selected algorithm is responsible for
determining the correct weight of each input in order to achieve the best estimations of the energy
consumption. The toolbox selected to develop this model, allows the user to use one of three different
training algorithms: Levenberg-Marquardt, Bayesian Regularization and Scaled Conjugate Gradient.
The main objective of the training algorithms, as said before, is to evaluate the best weigh distribution
of the parameters used, for any given test. And they achieve this goal, using and iterative process in
which the network performance function in minimized. The performance function used in the training
algorithms available is the means squared error (MSE), given by equation14.
𝑉(𝑥) =1
𝑁∑ 𝑒𝑞
2(𝑥)𝑁𝑞=1 ( 14 )
Where N is the size of the training dataset, and 𝑒𝑞 is the error (difference between the target and
predicted value) of the 𝑞𝑡ℎ input. With the main objective being to minimize the performance function,
lower values are desired and the optimal value, 0, means that there is no difference between the models
output and the target.
54
Although the objective of the three training functions is the same, the path each uses to achieve it is
what differs between them.
The experiments was divided in three different situations, according to the training algorithm used in
each one:
A. Levenberg-Marquardt
B. Bayesian Regularization
C. Scaled Conjugate Gradient
For each of the above cases the variable parameters were tested according to the following sets:
{Training Set, Validation Set, Test Set} = {70, 15, 15}, {60, 25, 15} and {50, 25, 25};
Hidden Neurons= {10, 20, 30};
The combination of the three algorithms with the different sets of parameters chosen above, resulted in
a total of 27 experiments, each one involving a total of 10 runs. Every experiment was made using a
specific toolbox available with MATLAB, the code developed is available in Annex IV. The results will be
analysed in terms of finding the lowest value of MSE mean and standard deviation.
As the aim is to minimize the performance function, the lowest MSE mean and standard deviation are
desired. In the following tables, the results are evaluated like:
1) The sets highlighted by a green colour are the favoured ones when the training, validation and
test sets, and the train algorithm are fixed;
2) The sets whose box is with grey background are the favoured ones when the train algorithm is
fixed (analyse the entire column regarding each train algorithm). It consists in best green MSE
configuration of the column;
3) The set whose MSE values are larger and highlighted in green is the ideal configuration of all,
being the best amongst the grey background ones.
Training set: 70 % Validation set: 15 % Test set: 15 %
Train Algorithm Levenberg-Marquadt Bayesian
Regularization
Scaled Conjugate Gradient
MSE Mean Std. Dev Mean Std. Dev Mean Std. Dev
Hidden
Neurons
10 34.4346 8.9878 30.5372 0.3570 35.7941 6.1885
20 45.7016 38.1621 30.4702 0.2298 47.9224 29.4893
30 62.3711 59.7110 29.5967 4.0936 40.5492 8.3568
Table 6-8- Results of the experiments for the first training set, {70,15,15}
55
Training set: 60 % Validation set: 25 % Test set: 15 %
Train Algorithm Levenberg-Marquadt Bayesian
Regularization
Scaled Conjugate Gradient
MSE Mean Std. Dev Mean Std. Dev Mean Std. Dev
Hidden
Neurons
10 32.0591 6.7105 30.5404 0.1971 35.7939 6.8480
20 40.5160 23.5518 30.3926 0.1564 43.2853 9.5811
30 56.4868 42.3390 29.7978 4.2247 36.2383 12.9626
Table 6-9- Results of the experiments for the second training set, {60,25,15}
Training set: 50 % Validation set: 25 % Test set: 25 %
Train Algorithm Levenberg-Marquadt Bayesian
Regularization
Scaled Conjugate Gradient
MSE Mean Std. Dev Mean Std. Dev Mean Std. Dev
Hidden
Neurons
10 55.4423 44.9845 30.4274 0.5249 34.3451 4.7178
20 37.4856 11.8003 30.4888 0.1447 40.5401 7.4334
30 50.1542 23.9479 30.4519 0.2579 44.4799 24.6593
Table 6-10- Results of the experiments for the third training set, {50,25,25}
Based on the information displayed in Table 6-8 to Table 6-10, the data set distribution that better
represents the energy consumption for this case study is, training set= 60%, validation set=25% and
test set=15%. The training algorithm with the best results is Bayesian Regularization and it is best
configuration the NN uses 20 hidden neurons.
For the selected configuration an analysis was made to evaluate the behaviour of the model. For that
purpose, graphics showing the regression, R, that represents the correlation between the output and
the targets were plotted. The following results were obtained using the randomly selected training and
test sets of respectively 50% and 25% of the total data.
56
Figure 6.7- Correlation between the target and output for the training, test and overall sets
As seen in Figure 6.7 the correlation between the targets and the outputs for the selected sets of data
appears to be considerably low. A correlation close to 1 would be ideal as it would mean the model was
estimating energy consumptions perfectly. This results appear to be incoherent to the results seen when
choosing the best training algorithm, sets of data and number of hidden neurons.
The low correlation factor as to do with the previously explained fact that the data gathered in the
company is composed by a large number of measures in small capacity machines. This fact combined
with the random nature of the sets selection results in a model that provides good estimations for small
machines, but is unable to do it for larger capacity machines.
In parallel with the development of the neural network model an analysis was conducted to evaluate the
importance of the inputs selected. This analysis allows the creating of a ranking placing the variables
given as the most influent on the energy consumption.
With this objective, using the data set gathered at the company (180 measures), the variables were
ranked in terms of importance to the model using a k nearest neighbour algorithm.
Installed power Cycle time Total mass Material
1 2 3 4
Table 6-11- Key variables used in the model ranked in terms of influence in the results
Similarly to what was done with the other tested models, the developed neural network model was tested
using both the data gathered and the complete set of data (222 measures), combining original measures
with the ones available on previous works [12], [15].
To achieve this goal a new selection of the data sets distribution, training algorithm and number of
neuron present in the hidden layer was made. The results were interpreted as before.
57
Training set: 70 % Validation set: 15 % Test set: 15 %
Train Algorithm Levenberg-Marquadt Bayesian
Regularization
Scaled Conjugate
Gradient
MSE Mean Std. Dev Mean Std. Dev Mean Std. Dev
Hidden
Neurons
10 93.3004 28.0784 83.1292 111.3426 113.3437 32.7345
20 121.4437 116.1017 80.7713 68.6918 115.4658 42.2734
30 210.0287 370.3890 82.5848 153.1040 145.5127 52.9062
Table 6-12- Results of the experiments for the first training set, {70,15,15} [Combined data]
Training set: 60 % Validation set: 25 % Test set: 15 %
Train Algorithm Levenberg-Marquadt Bayesian
Regularization
Scaled Conjugate
Gradient
MSE Mean Std. Dev Mean Std. Dev Mean Std. Dev
Hidden
Neurons
10 135.3836 75.2371 68.3650 93.0471 131.2592 58.9495
20 126.0701 89.4438 121.3876 139.0588 120.3454 53.4396
30 109.4847 43.3885 158.9578 281.7223 120.2134 25.3590
Table 6-13- Results of the experiments for the first training set, {60,25,15} [Combined data]
Training set: 50 % Validation set: 25 % Test set: 25 %
Train Algorithm Levenberg-Marquadt Bayesian
Regularization
Scaled Conjugate
Gradient
MSE Mean Std. Dev Mean Std. Dev Mean Std. Dev
10 101.6435 37.3302 76.0914 41.1872 127.5179 28.0823
58
Hidden
Neurons 20 198.2700 122.3784 75.7302 42.0561 170.0482 50.4229
30 265.9584 247.9895 111.3106 101.6935 199.9640 73.4575
Table 6-14- Results of the experiments for the first training set, {50,25,25} [Combined data]
Analysing the information available on Table 6-12 to Table 6-14 it’s possible to select the best
configuration for de different sets of data, training algorithm and number of neurons in the hidden layer.
The best configuration is similar to the one selected in the previous case with de only difference being
the number of neurons in the hidden layer, 10 instead of 20. The distribution is, training set= 60%,
validation set=25% and test set=15%. The training algorithm with the best results is Bayesian
Regularization and it is best configuration the NN uses 10 hidden neurons.
A more detailed analysis of the table reveals that in this case (three companies) the mean squared error
is considerably higher, 68.36, and the standard deviation is much more significant than in the previous
case, 93.04.
As before, for the selected configuration an analysis was made to evaluate the behaviour of the model.
For that purpose, graphics showing the regression, R, that represents the correlation between the output
and the targets were plotted. The following results were obtained using the randomly selected training
and test sets of respectively 50% and 25% of the total data.
Figure 6.8- Correlation between the target and output for the training, test and overall sets for the combined data set
By comparison with the correlation factors found in the previous set of data, the combination of the three
companies promotes considerably higher correlations. In fact, the R values in the three cases (training,
test, all) are fairly close to the ideal value of 1. The difference can be explained by the fact that this larger
array of data includes measurements for larger capacity machines, making the destitution more even
throughout the spectre.
59
Gathered data Combined data
MSE 30.39 68.36
STD. Dev 0.16 93.04
R (training set) 0.5 0.96
R (test set) -0.08 0.90
R (overall set) 0.48 0.95
Table 6-15- Comparison of the MSE and standard deviation results in both the single and the combined data set
Table 6-15 displays a comparison between the results obtained using neural networks for both the
proposed data sets. Comparing the results draws the conclusion that the model provides lower MSE
values for the simpler data set, and that in this case the standard deviation is negligible when compared
to the values obtained for the three companies.
Even though the initial case study appears to provide better estimations of the energy consumption, this
array of data lack in measurements for larger machines, resulting in a model that is incapable to estimate
energy consumptions for those machines. The second case study, reveals itself worst when modelling
energy consumptions but allows the estimation of this output for larger capacity machines.
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7. Discussion
In this chapter the results of the approaches selected to model energy consumption in the injection
moulding are discussed. It is also discussed the applicability of each model as well as their limitations
and the causes behind them.
7.1. SEC Model
The proposed SEC model appears to be a useful tool in energy consumption modelling and prediction.
It can provide good estimations when it is developed for a specific company, using data from the
machines available there. Furthermore, it can be applicable when a company uses a generic value,
such as the one calculated on this thesis. Ideally a company should develop SEC values for each
machine individually, based on a sample of several injected parts, and use that value to predict the
consumption of new parts to be injected in that machine for comparing machines and serve as a tool to
improve the process planning in the early design phase.
Obviously the estimations obtained with this model have errors when compared to the real values, but
can provide useful estimations of the energy consumption in this process with considerable reliability.
SEC models are relatively effortless and inexpensive do develop as they can be assessed using small
quantities of data. The main cost associated with developing this models is the purchase of the
equipment necessary to measure energy consumptions.
Gathering energy consumption it is a fairly simple process and with time, a company can obtain large
quantities of data, improving the precision of the model. Systems able to assure continuous monitoring
of energy and mass flow, such as the one found in the company visited, allow faster processes of data
gathering. This systems also provide the ability to continuously update the SEC reference values and
maintaining higher precision when estimating consumptions.
One of the biggest limitations of SEC models is the downside of its biggest advantage. The limited
amount of inputs required is what makes it simple to develop and to use SEC models, but is also one of
its major limitations. By having few inputs, the model doesn’t consider important factors to the energy
consumption. In the literature it was found that the geometry of the part can have a big influence when
it comes to the power used in the process, fact that is not totally considered by the approach in question.
The size and geometry of a certain part is limited in maximum dimensions by the machine used but the
part/machine fitness is not accounted in this model.
61
Although there were found significant differences in the data gathered in different companies, the
proposed SEC model reacts well, maintaining close values of average specific energy consumption
between the three companies. Even when compared with the value obtained by Gutowski [27], the
average value is similar. This evidence assures the usability of calculated SEC average in different
companies with acceptable precision.
Given what was said before, the SEC model developed is proposed as a tool to obtain quick and simple
estimations with a fairly good precision, especially when developed with large quantities of data and
used within the same company.
7.2. Process-Based Model
In an attempt to fill the limitations left by the low number of inputs used by SEC models, it was tested a
process-based model. Process-based models are very sensitive to the characteristics of each process,
and achieve this by integrating a larger number of inputs.
From the literary research, the selected model was the Ribeiro et al. [12]. This model appears to be the
most complete for the injection moulding process, and it was developed originally with a serious
consideration by the industrial environment, including several energy consumption measures. This
model was also further tested and developed by Domingues [15].
Even though the model was developed in as industrial environment, it is originally created using a
relatively small set of data, and was further tested by Domingues [15] with a larger but still considerably
small set. This fact led to interest of studying the results and applicability of this model with larger sets
of data. Thanks to the live resource monitoring system available ate the company, it was possible to
achieve this goal.
The application of such model on a larger set of data led to a group of new conclusions. The model has
limitations and could not model properly the energy consumption in the visited company.
The company visited during this thesis has wide variety of machines. Some big, but mostly smaller
machines. Besides that, each smaller machine uses different moulds and injects several different parts
while the larger equipment is usually associated with large scale production of a single part. This fact
resulted in a considerably higher amount of data for small machines, which does not appear to be helpful
when applying this model.
Another consideration taken from the data available is the fact that unlike the two companies visited in
previous works, the visited company has machines from several different manufacturers and with
different ages. The data used by Ribeiro et al. [12] was taken in a group of machines from the same
62
manufacturer and had approximately the same age. The same can be said for the work developed by
Domingues [15].
The model proposed by Ribeiro et al. [12] is still a useful way of predicting energy consumptions if
developed with consistent set of data, as shown by Domingues [15]. Its application requires attention to
the two fact explained above. With the right set of data, containing identical parts, machines and
production focus, this model can be used to provide high precision estimations of energy consumption.
7.3. Neural Networks Model
Given the possibility to gather larger sets of data, provided by the monitoring system installed in the
visited company, there came the idea of applying neural networks to this experimental case.
Neural Networks are complex systems able to solve nonlinear problems, however, they are made
relatively simple to develop due to the established knowledge and tools available with this purpose. It is
a question of defining the right input variables and providing sufficient data for the model to be trained,
tested and validated. The main requirement for developing this models is gathering enough data to
develop it, and like the SEC models, this is considerably easier when a continuous monitoring system
is available.
The NN-based model developed in this thesis displays the same problems as the ones seen in the
applied PBM. The uneven distribution of the data in the machine capacity spectrum results in the
incapability of estimating energy consumptions for larger machines. This model could use a more evenly
distributed set of data, with more measurements for larger machines. Despite this limitation, the model
appears to provide good estimations for smaller capacity machines, with low error and negligible
standard deviation.
The proposed NN model allows the estimation of energy consumptions for companies other than the
one where the data to develop was gathered, as seen with the data from the three companies combined.
However, much like the previous models it benefits from being developed with data from the company
where it will be used.
Overall, neural networks models can be really useful in energy estimation. They provide simple yet
precise energy estimations if the right inputs are chosen and if the data used in the development is
coherent.
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8. Conclusion
This work was developed having the context of resources efficiency and production costs in mind. For
that factor, energy consumptions modelling technics were tested and developed to improve the precision
of the forecasting and evaluate the specific applicability of each model. The injection moulding process
was studied and the parameters with influence on the power required for the process were listed and
tested using experimental data.
Three approaches were selected to model energy consumptions. Two of them were tested and
compared to previous works and the third was adapted to the case of the injection moulding industry.
The first is based on a SEC approach and includes three different tests to that metric. In an initial
analysis, a SEC reference value was calculated, using the average of the specific energy consumption
of the totality of the experimental measurements. In an attempt to improve the precision of this model,
new reference values were calculated using the average of the individual measurements divided by
machine clamping force and by material. This attempt revelled itself unable to improve the precision of
the traditional SEC model.
Motivated by the limitation found on SEC models, there was the need to test more comprehensive
models. For this, a process based model was tested. This model is sensitive to the properties of the
injected part, the material, the machine, the processing conditions and the machine/part fitness, and
achieves this by using two parcels. The first is a thermodynamic model that considers the melting and
the filling part of the process. The second is a machine model that accounts for the rest of the
parameters. This model was tested before with good results, however, in the context of the visited
company it revelled itself inapplicable. Not only was it incapable of achieving predictions with acceptable
error but it was also impossible to adapt the coefficients to the data from the visited company.
One possible reason for the high deviation of the PBM estimations to the measured values is the set of
data used. The company where the data was gathered have a large number of small injection machines
and only few larger ones, this led to a big concentration of the measurements taking place in smaller
machines. Another factor is the age of machines found in the company. The visited company has
machines with varying ages, some fairly new and others considerable old. Other possibility is the
complexity of the injected parts, the measurements taken include simple parts such as tubes, and more
complex parts. The model does not account for details in this area.
To take advantage of the large set of data gathered, a new methodology was created using neural
networks. This models, however used before for energy modelling, had not been adapted to the injection
moulding industry prior to this work. When using NN’s a large set of data is necessary to allow the model
64
to learn from the measurements and improve the precision given by it. It was found a similar problem to
the process based models. The distribution of the measurements in the various dimension of the
machines means that the NN model is incapable of estimating energy consumptions for larger machines.
However, in smaller machines the model appears to provide fairly good estimations. The model was
also tested with data from other companies, resulting in an improvement in the capacity of modelling
larger machines energy consumption but a decrease in the overall precision.
The first and the third approach, both have positive results for the case of the visited company. The SEC
approach is preferred when the objective is to provide fast estimations requiring few variables and
development time. Neural networks can be very interesting in the injection moulding industry as they
can solve complex problems, however, this models require more data and a more evenly spread set of
data.
65
9. Future Work
The work developed in this thesis raised some consideration about future work to develop in this field.
The first suggestion is to improve the studies done around the geometry of the part. As seen during this
thesis the existing models could benefit of considering with more detail the geometry of the part.
Especially the effect it has on the cooling time and therefore on the cycle time of the injected part. The
fitness of certain injected parts in the machines used should also be reviewed.
For the developed SEC model, it is suggested that new reference values are created for each machine.
By using several measurements for the same machine injecting different parts it would be possible to
calculate a specific SEC value for each machine. Is possible that this leads to a big improvement on the
precision of the model.
In the case of the process based model, there is still the need to test it with large sets of data, and this
should be done using measurements distributed evenly in machines with different dimensions.
For the neural networks model it is recommended the development of a new model considering an even
larger set of experimental data with more evenly distributed measurements throughout the machine
dimension spectre. This models could benefit from being used with monitoring systems, such as the one
found in the visited company. By receiving new measurements from the monitoring system the model
could be constantly updated and the data set could be expanded to better train the model, thus
improving the precision of the estimations.
66
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69
11. Annex
11.1. Annex I- Data Gathering Sheet
1 2 3 4 5 6 7 8 9
Máquina
Marca/Modelo
Potência instalada [kW]
T1 [ºC]
T2 [ºC]
T3 [ºC]
T4 [ºC]
T5 [ºC]
T6 [ºC]
Cforce [ton]
Molde Nº Cavidades
Peça
Espessura máxima [mm]
Massa peça [g]
Massa gito [g]
Processo Tc [s]
Ta [s]
Material
Material
P_inj [bar]
ρ [g/cm^3]
Cp [kJ/kgºC]
Hf [J/g]
λ
Tmelt [ºC]
Medição Pexp [kW]
Nº da medição
Inf. Peça/Molde
Nº do molde
Nº Peça
70
11.2. Annex II- Machine List
Máq. Marca Ano Fabrico Modelo Potência
(kW)
Distância entre
colunas (mm)
Força fecho
(ton)
Cap.
Injeção
(cm3)
MA29 Sandretto 1979 6GV110TON 45,0 350 110 250
MA30 Sandretto 1979 6GV70T 30,0 300 70 170
MA31 Sandretto 1979 6GVT170TON 45,0 400 170 383
MA32 Sandretto 1979 6GVT170TON 45,0 400 170 383
MA36 Sandretto 1979 6GVT170TON 45,0 400 170 383
MA38 Sandretto 1979 6GVT170TON 45,0 400 170 383
MA40 MIR 1991 RMP 135/360 35,0 420 135 330
MA41
Negri
Bossi 1978 200 TON 40,0 500 200 990
MA42 Sandretto 1982 6GV50TON 20,0 265 50 72
MSA44 Sandretto 1996 SERIE OTTO 400 67,3 650 353 1370
MSA45 Sandretto 1996 SERIE OTTO 400 72,3 650 353 1370
MA46 Victor 1998 VS-50 21,0 310 50 87
MA47 Victor 1997 VS-130 25,6 460 130 265
MA49 MIR 1998 RMP140 34,5 465 140 412
MA50 MIR 1998 RMP280 68,0 625 280 904
MSA51 Sandretto 1998 SERIE OTTO 380 68,0 650 353 1370
MA52 MIR 1995 RMP65 21,5 320 58 108
MA53 Victor 1998 VS-50 21,0 310 50 87
MA54 Victor 1998 VS-50 21,0 310 50 87
MA55 Victor 1998 VS-80 24,5 360 80 163
MA56 Victor 1998 VS-80 24,5 360 80 163
MA57 Victor 1998 VS-130 25,6 460 130 165
MA58 Victor 1998 VR-350H 71,0 700 350 1160
MA59 MIR 1998 MPO 35 16,0 275 35 59
MA60 MIR 1998 MPO 35 16,0 275 35 59
MSA61 Victor 1999 VR-550T 87,0 850 550 2544
MA62 MIR 1999 RMP280 68,0 625 280 904
MA63 MIR 1999 RMP140 34,5 465 140 412
MA64 MIR 1999 RMP80 29,0 370 80 238
MA65 MIR 2000 RMP80 29,0 370 80 238
MA66 MIR 1999 RMP100 29,0 370 100 238
MA67 MIR 2000 RMP60/95 22,5 320 60 108
MA68 MIR 2000 RMP60/95 22,5 320 60 108
MSA69 MIR 2000 RMP830 217,0 930 830 4560
MA70 MIR 2000 RMP100 29,0 370 100 238
71
MA71 Victor 2000 VS-100 24,0 410 100 147
MA72 Victor 2000 VS-100 24,0 410 100 147
MA73 Victor 2000 VS-100 24,0 410 100 147
MA74 Victor 2001 VS-100 24,0 410 100 147
MA75 Victor 2001 VS-100 24,0 410 100 147
MA76 Victor 2001 VS-50 21,0 310 50 87
MA77 MIR 2002 RMP200 53,0 540 200 617
MA78 MIR 2003 RMP60/95 22,5 320 60 108
MA79 MIR 2003 RMP140 34,5 465 140 412
MA80 Victor 2003 VS-50 21,0 310 50 87
MA81 Victor 2003 VS-100 24,0 410 100 147
MSA82 Victor 2003 VR-550T 87,0 850 550 2544
MSA83 Sandretto 1987 SERIE 7 650 128,0 820 650 2860
MA84 MIR 2004 RMP280 68,0 625 280 904
MA85 MIR 2004 RMP200 53,0 540 200 617
MA86 Victor 2004 VS-100 24,0 410 100 147
MA87 Victor 2004 VS-100 24,0 410 100 147
MA88 Victor 2004 VS-100 24,0 410 100 147
MA89 Sandretto 1994 SERIE SETTE 40 15,2 265 40 105
MSA90 Maico 2006 M-L-650T 160,0 910x800 650 2900
MA91 Maico 2006 SAVING 180 40,0 500 180 451
MA92 Maico 2006 SAVING 260 60,0 600 260 650
MSA93 Maico 2006 M-L-650T 160,0 910x800 650 2380
MA94 Sandretto 2007 SERIE9 1000/1200 26,3 415 100 214
MA95 Sandretto 2007 SERIE9 2200/860 43,1 510 220 475
MA96 Sandretto 1995 SERIE OTTO 85T 29,0 370 85 286
MA97 Sandretto 2007
SERIE9 -
650HP165 32,4 460 165 319
MSA98 Maico 1994 M-L-650T 143,0 820 650 3897
MA99 Maico 1996 380T 84,0 640 380 1455
MA100 Maico 2000 TEM 240 44,0 560 240 792
MA101 Maico 2000 TEK-S 320 80,0 650x580 320 1270
MSA102 Maico 2011 M-L-650 150,0 910 x 800 650 2380
MSA103 Maico 2012 SAVING 650 142,0 960x960 650 2670
MA104 Victor 2012 Victor 250 44,0 610x610 250 190
MA105 ENGEL 2001 ES 500/110 HL 27,7 110 251
MA106 ENGEL 2001 ES 200/45 HL 17,8 45 99
MA107 ENGEL 2001 ES 500/110 HL 27,7 110 251
MA108 ENGEL 2002
ES 200/45 HL -
Victory 17,8 45 99
72
MA109 ENGEL 2001 ES 1050/200 HL 46,3 200 510
MSA110 MAICO 1996
CORSA 600 EL
NR 29 108,0 805x805 589 2620
MAA111 MAICO 2006 SPRINT 300 153,0 660x600 300 805
MSA112 MAICO 2000 TEK-S 520 107,0 810x740 520 2550
MA113 Victor 2015 VS-100
73
11.3. Annex III- Experimental data
Maquina 42 89 55 54 60 65 66 76 73 97 38 87 47
Potência instalada [kW] 20 15,2 24,5 21 16 29 29 21 24 32,4 45 24 25,6
Força de fecho [ton] 50 40 80 50 35 80 100 50 100 165 170 100 130
Tbico [ºC] 0,3 0,65 185 200 230 229 210 181 196 0,55 0,3 177 180
T1 [ºC] 180 190 190 230 192 239 199 189 189 225 200 190 190
T2 [ºC] 190 200 190 231 183 240 210 180 199 230 200 200 188
T3 [ºC] 180 180 180 220 164 229 190 180 185 220 190 178 190
T4 [ºC] - - - - - - - - - 210 - - -
Tempo de ciclo [s] 15,55 14,5 24,3 20,6 21,44 47,02 19,95 15,4 25,5 25,5 45,98 24,3 36,1
Tempo de arref. [s] 8 6 8 6 10,71 20,01 4,01 8 9 10 30 6 14
Nº de cavidades 2 6 2+2+1 2+2 4 2 1 1 1 1 1 2 2
Peça
Massa peça [g] 2,2 0,33 2,92/2,27/0,68 1,86/0,75 0,73 25,46 26,5 20,1 20,87 65,74 117,64 13,96 32,37
Massa gito[g] ? 1,94 5,55 5,17 2,29 2,94 N/A N/A N/A 3,96 2,34 3,91 N/A
Massa total [g] 5,56 3,92 16,61 10,39 5,21 53,86 26,5 20,1 20,87 69,7 119,98 31,83 32,37
Espessura máxima [mm] 4,4 3,13 10,1 1,8 4,3 ? 3,5 ? 4 2,4 ? ? ?
Material PP PP POM ABS POM PS PP POM POM PS PP POM POM
Potência exp [kW] 8,780488 7,804878 6,243902 14,63415 10,2439 13,17073 16,58537 5,454545 8,429752 7,933884 18,53659 8 9,756098
Maquina 74 94 96 32 56 55 54 67 64 89 40 42 32
Potência instalada [kW] 24 26,3 29 45 24,5 24,5 21 22,5 29 15,2 35 20 45
Força de fecho [ton] 100 100 85 170 80 80 50 60 80 40 135 50 170
Tbico [ºC] 180 0,5 0,4 0,4 180 180 216 210 230 0,7 220 0,7 0,4
T1 [ºC] 199 210 195 220 190 190 250 180 235 200 220 190 220
T2 [ºC] 200 210 200 230 200 200 250 185 240 190 235 190 230
T3 [ºC] 185 190 180 210 180 180 230 170 225 170 220 180 210
74
T4 [ºC] - 180 - - - - - - - - - - -
T5 [ºC] - - - - - - - - - - - - -
Tempo de ciclo [s] 21,3 30,2 32 28,49 20,2 22,3 34,4 19,2 46,54 21 29,5 16,06 22,12
Tempo de arref. [s] 5 15,3 8,5 17 10 4 14 4,01 22,01 10 15 7 10
Nº de cavidades 4 4 8 1 2 1+1 1+1+1+1 1 1+1 1 1 4 1+1
Peça
Massa peça [g] 10,03 15,3 7,06 45,5 3,85 8,2 ? 16,8 84,23+60,7 7,2 22 1,53 5,61+27,13
Massa gito[g] 10,1 N/A 18,85 - 1,5 N/A ? N/A 13,04 N/A 1,5 1,34 6,76
Massa total [g] 50,22 61,2 75,33 45,5 9,2 16,4 13,25 16,8 157,97 7,2 23,5 7,46 39,5
Espessura máxima [mm] 7,5 ? 4,5 ? 7,8 9 3,51 ? 3,5 ? 5 2,5 2,5
Material PP POM PP ABS PP POM ABS POM PS PP POM PP ABS
Potência exp [kW] 8,292683 13,17073 8 13,38843 8,648649 6,27027 14,87603 34,05405 9,72973 7,837838 10,37838 7,135135 15
Maquina 92 78 79 76 88 47 71 70 38 49 99 95 94
Potência instalada [kW] 60 22,5 34,5 21 24 25,6 24 29 45 34,5 84 43,1 26,3
Força de fecho [ton] 260 60 140 50 100 130 100 100 170 140 380 220 100
Tbico [ºC] 0,4 200 240 180 177 180 185 211 0,3 220 0,3 0,35 0,2
T1 [ºC] 248 190 210 190 190 189 198 220 200 190 205 210 199
T2 [ºC] 250 200 213 195 200 199 188 230 200 200 211 200 200
T3 [ºC] 242 170 190 180 190 190 180 200 190 180 216 200 180
T4 [ºC] 226 - - - - - - - - - 190 185 180
T5 [ºC] - - - - - - - - - - - - -
Tempo de ciclo [s] 22,5 22,3 35,48 17,6 30 45,8 15,3 32,22 37 29,27 70 15,7 25
Tempo de arref. [s] 3 9 19 2 10 25 3 13,01 20 15,01 15 4 10
Nº de cavidades 2 2 2+2 4 8 4 4 1 1+1+1 2 2 4 8
Peça
Massa peça [g] 25,4 27,11 24,1+25,22 3,625 0,99 19,7 6,79 76 32,56//1,59//5,391 35,05 242,7 15,36 4,5
Massa gito[g] N/A 1,79 5,94 N/A 3,07 N/A N/A N/A - N/A - N/A N/A
75
Massa total [g] 50,8 56,01 104,58 14,5 10,99 78,8 27,16 76 49,541 70,1 485,4 61,44 36
Espessura máxima [mm] 4 1,7 ? 5 4,5 12,3 4,85 ? 11 2 ? 4 3,9
Material ABS PP PP POM POM POM POM ABS PP PP PP PP POM
Potência exp [kW] 13,25967 9,281768 14,25414 7,624309 9,281768 11,27072 9,421488 16 12,48 10,93923 16,95652 19,56522 7,304348
Maquina 72 76 75 79 77 105 78 81 96 46 57 55 53
Potência instalada [kW] 24 21 24 34,5 53 27,7 22,5 24 29 21 25,6 24,5 21
Força de fecho [ton] 100 50 100 140 200 110 60 100 85 50 130 80 50
Tbico [ºC] 205 193 180 190 230 190 228 230 0,25 200 2,25 183 160
T1 [ºC] 215 200 190 180 244 200 190 220 190 250 245 189 185
T2 [ºC] 225 200 190 180 250 200 200 210 200 255 245 190 190
T3 [ºC] 217 180 180 175 230 190 170 200 180 235 230 180 170
T4 [ºC] - - - - - - - - - - - - -
T5 [ºC] - - - - - - - - - - - - -
Tempo de ciclo [s] 34,2 18 25,2 27,36 28,24 40,52 19,56 25,5 31 16,1 19,2 20,9 21,3
Tempo de arref. [s] 14 5 10 10 10 12,02 9 5 17 4 5 10 6
Nº de cavidades 2 8 2 1+1 2 2 2 1 4 1 1+1 1 10
Peça
Massa peça [g] 22,04 2,34 21,78 31,31+65,79 70,66 68 14,45 42,5 5,9 8,514 8,29 13,8 4,09
Massa gito[g] 5,88 N/A 8,55 10,36 N/A N/A 1,94 N/A N/A N/A N/A N/A N/A
Massa total [g] 49,96 18,72 52,11 107,46 141,32 136 30,84 42,5 23,6 8,514 16,58 13,8 40,9
Espessura máxima [mm] 4 6 6 ? ? 7 3,4 6 17,3 5,1 7 ? 5,8
Material PS POM POM PP PS PP PP PP PP ABS ABS POM POM
Potência exp [kW] 3,809524 7,741935 9,756098 15 8,292683 9,193548 8,709677 8,709677 8,648649 5,25 11,89831 5,694915 19,83051
76
Maquina 56 65 63 32 40 40 89 57 53 60 59 67 64
Potência instalada [kW] 24,5 29 34,5 45 35 35 15,2 25,6 21 16 16 22,5 29
Força de fecho [ton] 80 80 140 170 135 135 40 130 50 35 35 60 80
Tbico [ºC] 190 250 230 0,4 210 200 0,7 2,27 160 2,2 225 250 230
T1 [ºC] 200 240 230 230 230 235 195 245 185 200 220 230 230
T2 [ºC] 200 245 240 235 240 240 200 245 180 210 210 235 240
T3 [ºC] 180 230 220 220 220 230 100 230 170 195 200 217 218
T4 [ºC] - - - - - - - - - - - - -
T5 [ºC] - - - - - - - - - - - - -
Tempo de ciclo [s] 25,9 41,12 25,51 26 24 41,8 13 19,5 21,4 20,81 17,84 37,63 26,03
Tempo de arref. [s] 15 17,01 10,01 16 12 30 5 5 6 10,01 4,01 15,01 10,01
Nº de cavidades 4 1+1 2 1 1+1 2+2 2 1+1 10 4 1+2 2 2
Peça
Massa peça [g] 3,075 3,6+2,72 50,7 28,96 20,1//16,64 8,5 + 19,41 1,19 11,74+8,38 4,09 5,96 3,29+0,32 18,08 1,16
Massa gito[g] 6,84 ? 12,26 N/A 2,83 5,19 0,91 n/a n/a 4,94 1,45 3,61 -
Massa total [g] 19,14 6,32 113,66 28,96 39,57 61,01 3,29 20,12 40,9 28,78 5,38 39,77 2,32
Espessura máxima [mm] 2,65 ? ? 2,5 10 5 2 7 5,8 8,9 5 15,5 1,25
Material PP ABS ABS ABS PS PS PP ABS POM POM PP PP ABS
Potência exp [kW] 11,18644 14,0339 8,644068 13,72881 9,762712 9,917355 8,181818 11,60331 18,84298 11,60331 16,32 39,42149 10,16529
Maquina 68 65 92 100 72 73 78 97 62 70 51 45 69
Potência instalada [kW] 22,5 29 60 44 24 24 22,5 32,4 68 29 68 72,3 217
Força de fecho [ton] 60 80 260 240 100 100 60 165 280 100 353 353 830
Tbico [ºC] 230 220 0,6 0,3 208 190 200 0,5 2,55 200 200 0,2 180
T1 [ºC] 200 250 248 195 210 190 190 240 220 190 230 250 230
T2 [ºC] 200 250 245 205 220 190 200 240 215 180 240 250 250
T3 [ºC] 180 230 230 200 210 180 180 230 205 166 230 235 250
77
T4 [ºC] - - 211 195 - - - 210 180 - 210 225 240
T5 [ºC] - - - 180 - - - - - - - - -
Tempo de ciclo [s] 25,27 36,49 40,6 51,8 35,4 33,7 16,4 35,4 40,9 25,65 50,5 39,5 75,56
Tempo de arref. [s] 12,01 14,01 10 18 10 12 6 17 8,01 10,01 19 7 31,01
Nº de cavidades 2+2+2 2 2 2 2 2 4 1 2+2 1+1 1+2 4+4+4+4+4 2
Peça
Massa peça [g] 2,66+1,61+2,37 11,36 77,61 36,9 17,77 24,11 1,54 68,92 26,5 22,03+11,46 346,5 ? 1325
Massa gito[g] 5,57 n/a n/a n/a 5,86 n/a 1,58 N/A N/A 7,42 - - -
Massa total [g] 18,85 22,72 155,22 73,8 41,4 48,22 7,74 68,92 106 40,91 346,5 489,024 2650
Espessura máxima [mm] 2,59 5 3 13 2,5 ? 3,7 ? 4 ? ? ? 4,7
Material POM ABS PS PP PS POM PP PS PP POM PS POM PS
Potência exp [kW] 8,92562 12,49587 14,87603 32,72727 6,942149 8,429752 7,2 7,090909 8,429752 12,89256 11 33 36
Maquina 92 72 73 78 97 87 100 63 65 68 58 95 91
Potência instalada [kW] 60 24 24 22,5 32,4 24 44 34,5 29 22,5 71 43,1 40
Força de fecho [ton] 260 100 100 60 165 100 240 140 80 60 350 220 180
Tbico [ºC] 0,6 210 180 200 0,5 180 0,3 230 275 190 210 0,5 0,5
T1 [ºC] 230 230 185 190 240 190 195 230 240 190 205 195 200
T2 [ºC] 240 230 175 200 240 200 205 240 245 200 205 200 205
T3 [ºC] 240 220 170 170 230 100 200 230 230 180 195 190 195
T4 [ºC] 215 240 - - 210 - 195 - - - 195 175 185
T5 [ºC] - - - - - - 180 - - - - - -
Tempo de ciclo [s] 44,9 22,6 25,3 22,7 34,2 29,3 51 27,3 35,9 33,67 51,2 26,3 39,5
Tempo de arref. [s] 15 12 10 9 17 13 18 8,01 14,01 17,01 18 10 22
Nº de cavidades 8 1 8 2 1 4 2 1 2 4 1+1+1 4 2
Peça
78
Massa peça [g] 13 41,43 3,95 27,11 68,92 25,9 36,9 106,2 10,66 4,81 77+19,43+48,3 37,99 43,92
Massa gito[g] N/A 7,36 0,47 1,79 N/A N/A N/A N/A 3,63 N/A 9,35 N/A N/A
Massa total [g] 104 48,79 32,07 56,01 68,92 25,9 73,8 106,2 24,95 19,24 154,08 151,96 87,84
Espessura máxima [mm] 10,4 ? 4,32 1,7 ? 3,9 13 ? 4,35 1,9 ? ? 3,5
Material ABS PS POM PP PS POM POM PP ABS POM PP POM PP
Potência exp [kW] 13,75 8 12,5 10 7,5 9,75 33,71901 9,173554 12,79339 10,16529 12,39669 19,83471 4,640884
Maquina 93 73 104 62 70 113 86 87 74 96 29 46 53
Potência instalada [kW] 160 24 44 68 29 22,8 24 24 24 29 45 21 21
Força de fecho [ton] 650 100 250 280 100 100 100 100 100 85 110 50 50
Tbico [ºC] 0,3 180 240 255 255 200 190 200 180 0,4 0,45 220 171
T1 [ºC] 210 190 240 220 220 210 190 210 200 200 205 250 190
T2 [ºC] 220 200 240 215 215 210 190 210 205 200 210 255 190
T3 [ºC] 225 190 220 210 210 205 190 190 190 185 200 230 180
T4 [ºC] 215 - - 180 180 - - - - - - - -
T5 [ºC] 205 - - - - - - - - - - - -
Tempo de ciclo [s] 69,8 32,3 27 41,29 20,43 31,1 42,4 41,1 28,5 24,9 23 25,1 28,1
Tempo de arref. [s] 26 15 8 8,01 5,01 10 10 12 8 17 13 4 16
Nº de cavidades 1 1+1+1+1 4 2+2 8 4 4 1+1 1 2 2 2+2 2
Peça
Massa peça [g] 1025,95 ? 20,77 ? 3,36 ? ? 10,95+23,7 27,12 8,54 40,35 4,85+2,14 5,45
Massa gito[g] N/A N/A N/A N/A 4,13 N/A N/A N/A N/A 3,72 N/A 4,77 4,23
Massa total [g] 1025,95 30,823 83,08 64,58 31,01 31,74 35,6 34,65 27,12 20,8 80,7 18,75 15,13
Espessura máxima [mm] 3 5,6 9,63 4 4,8 6,8 4,5 6 5 ? 1,9 5,5
Material PP POM ABS PP POM POM POM POM PP PP PP ABS POM
Potência exp [kW] 10,60773 8,108108 15,86777 7,933884 14,4 6,942149 9,421488 9,421488 8,429752 7,933884 8,92562 4,561983 11,40496
79
Maquina 54 59 60 56 92 72 91 95 94 109 93 90 111
Potência instalada [kW] 21 16 16 24,5 60 24 40 43,1 26,3 46,3 160 160 153
Força de fecho [ton] 50 35 35 80 260 100 180 220 100 200 650 650 300
Tbico [ºC] 210 240 220 180 230 210 0,5 0,3 0,2 200 0,3 0,6 0,5
T1 [ºC] 215 200 191 190 240 240 200 190 190 190 205 185 230
T2 [ºC] 230 200 200 200 240 230 205 200 190 200 210 195 240
T3 [ºC] 230 170 175 180 220 220 195 190 180 190 215 190 240
T4 [ºC] - - - - - - 185 185 175 190 205 190 235
T5 [ºC] - - - - - - - - - - 197 183 220
Tempo de ciclo [s] 37 24,16 24,7 33,7 80 29,6 39 31,3 25,8 24,12 73 47,5 44,9
Tempo de arref. [s] 13 11,01 14,01 15 15 12 22 16 10 11,02 30 15 18
Nº de cavidades 2+2 1+1 2 8 8 1 2 4 4+4 2 1 1 2
Peça
Massa peça [g] ? ? 4,84 1,31 ? 22,78 43,92 7,19 1,67 // 6,85 41,41 992 876 259
Massa gito[g] ? ? 2,03 3,91 N/A 5,11 N/A N/A 1,96 N/A - N/A N/A
Massa total [g] 6,57 12,5465 11,71 14,39 104 27,89 87,84 28,76 36,04 82,82 992 876 518
Espessura máxima [mm] 2 9,35 ? 2,5 10,4 ? 3,5 7,16 5,51 2 ? 4,7 4,3
Material ABS POM POM PP ABS PS PP POM POM PP PP PP PS
Potência exp [kW] 18,51852 18,34711 14,28099 14,38017 14,87603 6,942149 4,958678 18,59504 6,446281 12,89256 9,917355 34,63918 20,33058
Maquina 85 101 103 31 29 89 57 46 53 60 63 64 32
Potência instalada [kW] 53 80 142 45 45 15,2 25,6 21 21 16 34,5 29 45
Força de fecho [ton] 200 320 650 170 110 40 130 50 50 35 140 80 170
Tbico [ºC] 240 230 190 220 0,45 220 227 215 170 192 220 2,5 240
T1 [ºC] 230 230 235 190 205 190 245 230 190 180 240 250 240
80
T2 [ºC] 240 260 240 200 210 200 245 240 190 163 240 250 240
T3 [ºC] 220 260 235 180 200 180 230 220 180 - 230 236 225
T4 [ºC] - - 220 - - - - - - - - - -
T5 [ºC] - - 215 - - - - - - - - - -
Tempo de ciclo [s] 35,09 56 100 37 23 10,5 18,8 28,6 28 22,1 37,61 42,25 31
Tempo de arref. [s] 14 18 35 23 13 3 4 15 16 10,71 15,01 15,01 15
Nº de cavidades 2 2 1 2+4 2 4 1+1 2 2 4 1 1+1+1 1
Peça
Massa peça [g] 40,4 140,2 ? 2X17,05+4X11,33 40,35 1,48 11,74+8,38 22,86 0,73 ? ? ?
Massa gito[g] N/A N/A ? 7,23 N/A 2,05 N/A 3,89 2,29 ? ? ?
Massa total [g] 80,8 280,4 1218,64 86,65 80,7 7,97 20,12 49,61 16,4 5,21 69,93 28,95 44,2
Espessura máxima [mm] 3 1,8 ? ? ? 1,45 ? ? 5,8 4,3 3,8 14,48 ?
Material PP ABS PS PP PP PP ABS ABS POM POM ABS ABS ABS
Potência exp [kW] 7,438017 5,950413 22,56637 14,0177 8,761062 8,495575 9,876106 5,097345 11,68142 14,0177 7,433628 10,61947 15,9292
Maquina 47 72 73 79 87 105 66 72 78 80 96 97 105
Potência instalada [kW] 25,6 24 24 34,5 24 27,7 29 24 22,5 21 29 32,5 27,7
Força de fecho [ton] 130 100 100 140 100 110 100 100 60 50 85 165 210
Tbico [ºC] 180 205 200 200 180 200 2,1 220 240 180 0,5 0,5 200
T1 [ºC] 190 215 200 200 200 190 200 240 210 200 210 240 190
T2 [ºC] 190 225 200 210 200 200 210 235 210 200 210 240 200
T3 [ºC] 180 220 190 200 190 180 190 230 190 180 190 230 180
T4 [ºC] - - - - - - - - - - 210 -
T5 [ºC] - - - - - - - - - - - -
Tempo de ciclo [s] 49 32,2 40,6 32,78 33,1 27,96 19,98 22,7 33,5 35,6 22 33,9 27,96
Tempo de arref. [s] 20 14 20 20 15 12,02 4,01 10 19 15 10 17 12,02
Nº de cavidades 1 2 2+1 1 1 2 1 1 2+2 1 4 1 2
Peça
81
Massa peça [g] 55,68 22,04 ? 41,54 22,78 21,89 26,51 24,94 24,1+25,22 - 7,46 68,92 17,67
Massa gito[g] 9,06 5,88 ? N/A 18,3 6,31 N/A N/A 5,94 - 3,56 N/A 4,59
Massa total [g] 55,68 49,96 49,4 41,54 41,08 50,09 26,51 24,94 104,58 5,55 33,4 68,92 39,93
Espessura máxima [mm] 10 6,1 4 3,5 3,3 4
Material POM PS POM PP POM PP PP PS PP POM PP PS PP
Potência exp [kW] 7,317073 4,778761 8,108108 10,81081 8,108108 7,027027 15,86777 8 9,5 7,5 10 7,5 6
Maquina 72 76 80 96 97 104 105 72 75 76 79 87 96
Potência instalada [kW] 24 21 21 29 29 81 27,7 24 24 21 34,5 24 29
Força de fecho [ton] 100 50 50 85 85 250 210 100 100 50 140 100 85
Tbico [ºC] 210 1,8 190 0,5 0,3 235 175 210 210 180 200 175 0,5
T1 [ºC] 240 190 190 210 240 240 200 240 200 190 200 190 210
T2 [ºC] 230 190 200 210 240 240 200 235 210 200 200 190 210
T3 [ºC] 220 100 180 180 240 240 190 230 190 180 195 170 190
T4 [ºC] - - - - 230 230 - - - - - - -
T5 [ºC] - - - - - - - - - - - - -
Tempo de ciclo [s] 28 21,3 21 19 29,5 26 27,81 21,5 32 28,2 27,6 17,6 23,3
Tempo de arref. [s] 12 12 8 5 11 8 12,02 8 13 16 12 5 10
Nº de cavidades 1 2 2 4 2 4 1 1 4 2 1 1 4
Peça
Massa peça [g] 22,78 3,11 - 63,86 17,67 24,94 - 4,36 65,79 4,71 6,88
Massa gito[g] 5,11 4,78 N/A N/A N/A 4,59 N/A N/A 3,78 10,36 2,31 3,09
Massa total [g] 27,89 5,04 11 10,93 127,72 35,79 22,26 24,94 41,06 12,5 76,15 7,02 30,61
Espessura máxima [mm] 8,47 4 2 1,5 4 2 5,8 - 3,5 3 -
Material PS POM POM PP PS ABS PP PS POM POM PP POM PP
Potência exp [kW] 8,181818 5,454545 4,909091 8,727273 9,272727 13,09091 7,090909 8,117647 7,058824 5,294118 14,11765 9,176471 8,470588
82
Maquina 97 30 36 47 66 70 81 88 90 91 94 95 109
Potência instalada [kW] 32,4 30 45 25,6 29 24 24 24 160 40 26,3 43,1 46,3
Força de fecho [ton] 165 70 170 130 100 100 100 100 650 180 100 220 200
Tbico [ºC] 0,4 210 0,45 180 200 240 180 160 50 195 0,3 190 200
T1 [ºC] 240 210 200 200 192 230 195 180 185 200 200 200 195
T2 [ºC] 240 255 200 200 200 230 200 180 195 190 200 190 200
T3 [ºC] 230 255 190 180 180 230 185 170 190 - 190 185 195
T4 [ºC] 210 - - - - - - - 190 - 185 - 190
T5 [ºC] - - - - - - - - 185 - - - -
Tempo de ciclo [s] 33,5 34 20 35 33,6 30 28,8 25 85 27 32,7 31,1 22
Tempo de arref. [s] 17 20 5 20 16 15 15 10 15 3 18 16 4
Nº de cavidades 1 2 6 2 2 1 4 8 1 2 8 4 2
Peça
Massa peça [g] 69,92 34,4 10,4 41,32 39,8 44,26 6,3 1 70,7 6,2 8,2 7,2 28,9
Massa gito[g] N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A - - -
Massa total [g] 69,92 68,8 62,4 247,92 79,6 44,26 25,2 8 70,7 24,8 65,6 28,8 57,8
Espessura máxima [mm] - 3 4,8 3,7 4 4,5 1 7,16 2
Material PS PP PP POM PP ABS PP POM PP PP POM POM PP
Potência exp [kW] 7,411765 7,368421 13,33333 11,57895 13,33333 13,33333 7,017544 9,824561 33,68421 4,210526 6,666667 16,37931 11,92982
Maquina 105 40 54 56 100 105 107 113
Potência instalada [kW] 27,7 35 21 24,5 44 27,7 27,7 22,8
Força de fecho [ton] 110 135 50 80 240 110 110 100
Tbico [ºC] 220 - - - - - - -
T1 [ºC] 260 220 220 200 195 220 200 200
T2 [ºC] 260 230 240 225 205 260 220 200
T3 [ºC] 245 235 250 235 200 260 230 200
T4 [ºC] - 219 230 220 195 245 220 180
83
T5 [ºC] - - - - 180 - - -
Tempo de ciclo [s] 40,88 31,3 37,1 21,7 50,1 38,9 36,51 21,4
Tempo de arref. [s] 18,02 15 15 10 18 17,02 15,02 5,8
Nº de cavidades 2 2 2 4 2 4 1 2
Peça
Massa peça [g] 37,8 15,1 10,3 2,27 37,77 8,49 72,47 ?
Massa gito[g] N/A 12,5 N/A 5,59 N/A 9,41 7,75 ?
Massa total [g] 75,6 42,7 20,6 14,67 75,54 43,37 80,22 24,4
Espessura máxima [mm] 3,7
Material PP PS ABS ABS PP POM PS PP
Potência exp [kW] 9,87013 13,41176 9,74359 6,38961 16,49123 10,2 7,234043 5,405405
84
11.4. Annex IV- Matlab Code
clear;clc
% filename = 'Medições.xlsx';
filename = 'MediçõesOLI_4.xlsx';
sheet = 1;
%Ler inputs
Potencia_instalada = xlsread(filename,sheet,'5:5');
Tempo_ciclo = xlsread(filename,sheet,'15:15');
Massa_total = xlsread(filename,sheet,'22:22');
[~,Material,~] = xlsread(filename,sheet,'B25:FV25');
%%
% Espessura = xlsread(filename,sheet,'23:23');
% Forca_fecho = xlsread(filename,sheet,'6:6');
%%Passar de string para numéricos
%%PP=1;POM=2;ABS=3;PS=4
for i=1:length(Material)
switch Material{1,i}
case 'PP'
Mat(1,i)=1;
case 'POM'
Mat(1,i)=2;
case 'ABS'
Mat(1,i)=3;
case 'PS'
Mat(1,i)=4;
case 'TPE'
Mat(1,i)=5;
85
case 'PMMA'
Mat(1,i)=6;
end
end
% Ler output
Potencia_exp= xlsread(filename,sheet,'34:34');
%% Variáveis input e output
x=[Potencia_instalada; Tempo_ciclo; Massa_total; Mat];
% x=[Potencia_instalada; Massa_total];
t=Potencia_exp;
%%
% x - input data.
% y - target data.
x = x;
t = t;
for i=1:10
% Choose a Training Function
% For a list of all training functions type: help nntrain
% 'trainlm' is usually fastest.
% 'trainbr' takes longer but may be better for challenging problems.
% 'trainscg' uses less memory. Suitable in low memory situations.
trainFcn = 'trainbr';
% Create a Fitting Network
hiddenLayerSize = 20;
net = fitnet(hiddenLayerSize,trainFcn);
% Setup Division of Data for Training, Validation, Testing
net.divideParam.trainRatio = 60/100;
net.divideParam.valRatio = 25/100;
86
net.divideParam.testRatio = 15/100;
% Train the Network
[net,tr] = train(net,x,t);
% Test the Network
y = net(x);
e = gsubtract(t,y);
net.performFcn = 'mse';
performance = perform(net,t,y)
mse(1,i)=performance;
% View the Network
% view(net)
% Plots
% Uncomment these lines to enable various plots.
% figure, plotperform(tr)
%figure, plottrainstate(tr)
% figure, ploterrhist(e)
% figure, plotregression(t,y)
% figure, plotfit(net,x,t)
end
mse_mean=mean(mse)
std_mse=std(mse)
% %%%%%%%%%%%%%%%%%%%%%% Para ver quais as variáveis mais determinantes
% xtrain=x(:,1:round(0.6*length(x)));
% xtest=x(:,1:(length(x)-length(xtrain)));
% ytrain=t(:,1:round(0.6*length(t)));
% ytest=t(:,1:(length(t)-length(ytrain)));
% f = @(xtrain, ytrain, xtest, ytest) sum(ytest ~= classify(xtest, xtrain, ytrain));
% fs = sequentialfs(f,x',t')
87
% %%
% [RANKED,WEIGHT] = relieff(x',t',100)