an integrated framework for die and mold cost estimation
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7/31/2019 An Integrated Framework for Die and Mold Cost Estimation
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DOI 10.1007/s00170-004-2084-9
O R I G I N A L A R T I C L E
Int J Adv Manuf Technol (2005) 26: 11381149
Nagahanumaiah B. Ravi N.P. Mukherjee
An integrated framework for die and mold cost estimation
using design features and tooling parameters
Received: 5 August 2003 / Accepted: 6 January 2004 / Published online: 2 February 2005
Springer-Verlag London Limited 2005
Abstract Tooling is an essential element of near net shapemanufacturing processes such as injection molding and die cast-
ing, where it may account for over 25% of the total productcost and development time, especially when order quantity is
small. Development of rapid and low cost tooling, combined
with a scientific approach to mold cost estimation and control,has therefore become essential. This paper presents an integrated
methodology for die and mold cost estimation, based on the con-cept of cost drivers and cost modifiers. Cost drivers include the
geometric features of cavity and core, handled by analytical cost
estimation approach to estimate the basic mold cost. Cost mod-
ifiers include tooling parameters such as parting line, presenceof side core(s), surface texture, ejector mechanism and die ma-
terial, contributing to the total mold cost. The methodology hasbeen implemented and tested using 13 industrial examples. The
average deviation was 0.40%. The model is flexible and can beeasily implemented for estimating the cost of a variety of molds
and dies by customizing the cost modifiers using quality functiondeployment approach, which is also described in this paper.
Keywords Cost estimation Die casting Injection molding
Quality function deployment
1 Introduction
Product life cycles today are typically less than half of those
in the 1980s, owing to the frequent entry of new products withmore features into the market. Manufacturing competitiveness is
Nagahanumaiah N.P. MukherjeeCentral Mechanical Engineering Research Institute,Durgapur, India
B. Ravi (u)Mechanical Engineering Department,Indian Institute of Technology,Bombay, Powai, Mumbai 400 0076, IndiaE-mail: bravi@me.iitb.ac.inTel.: +91-22-2576 7510Fax: +91-22-2572 6875
measured in terms of shorter lead-time to market, without sac-rificing quality and cost. One way to reduce the lead-time is by
employing near net shape (NNS) manufacturing processes, suchas injection molding and die casting, which involve fewer steps
to obtain the desired shape. However, the tooling (die or mold),
which is an essential element of NNS manufacturing, consumesconsiderable resources in terms of cost, time and expertise.
A typical die casting die or plastic injection mold is madein two halves: moving and fixed, which butt together during
mold filling and move apart during part ejection. The construc-
tion of a typical cold chamber pressure die casting die is shown
in Fig. 1.The main functional elements of the die and mold include
the core and cavity, which impart the desired geometry to theincoming melt. These may be manufactured as single blocks or
built-up with a number of inserts. The secondary elements in-clude the feeding system, ejection system, side core actuators
and fasteners. The feeding system comprising of sprue bush,runner, gate and overflow enables the flow of melt from ma-
chine nozzle to mold cavity. The ejector mechanism is used forejecting the molded part from the core or cavity. All the above
elements are housed in a mold base set, comprising of supportblocks, guides and other elements. Part-specific elements, in-
cluding core and cavity and feeding system are manufactured in
a tool room. Other elements are available as standard accessories
from vendors. Mold assembly and functional trials are conducted
by experienced toolmakers in consultation with tool designers.The tooling industry is presently dominated by Japan, Ger-
many, USA, Canada, Korea, Taiwan, China, Malaysia, Singapore
and India. The major users of tooling include automobiles, elec-
tronics, consumer goods and electrical equipment sectors. Plasticmolds account for the major share of tooling industry. About
60% of tool rooms belong to small and medium scale industriesworldwide [1]. The tooling requirement is over US$ 600 mil-
lion per year in India alone, with an annual growth rate of over
10% during the last decade. In India, the share of different types
of molds and dies is: plastic molds 33%, sheet metal punches
and dies 31%, die casting dies 13%, jigs & fixtures 13%, and
gauges 10% [2].
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Fig. 1. Construction of a typical pressure die-casting die
The tooling industry is increasingly facing the pressure to re-duce the time and cost of die and mold development, offer better
accuracy and surface finish, provide flexibility to accommodate
future design changes and meet the requirements of shorter pro-duction runs. To meet these requirements, new technologies like
high speed machining, hardened steel machining, process mod-eling, tooling design automation, concurrent engineering, rapid
prototyping and rapid tooling have been applied. For successful
operations and to maintain the competitive edge, it is necessary
to establish quantitative methods for cost estimation.Our current research aims at developing a systematic and in-
tegrated framework for development of rapid hard tooling (diesand molds) for injection molding and pressure die-casting ap-
plications. The necessity of a systematic cost estimation modelfor comparative evaluation of different routes to tooling develop-
ment motivated us to review the existing models, presented in the
next section, followed by our proposed methodology.
2 Previous work
There is considerable similarity in cost estimation approaches
used for product and tooling as reported in technical literature.
These approaches can be classified into five groups: intuitive,
analogical, analytical, geometric feature based and parametric
based methods, briefly reviewed here.In the intuitive method, the accuracy of cost estimation de-
pends on the cost appraisers experience and interpretations. The
estimation is usually performed in consultation with the tooldesigner. The estimator acquires the wisdom and intuition con-
cerning the costs through long association with die and mold
development. This method is still in practice in small workshops
and tool rooms.
In the analogical method, the cost of die and mold is esti-mated based on similarity coefficients of previous dies and molds
manufactured by the firm. In this technique, dies are coded con-
sidering factors such as die size, die material, complexity, ejector
and gating mechanism. The appraiser starts by comparing thenew die design with the closest match among all previous de-
signs. The basic hypotheses are: similar problems have similar
solutions, and reuse is more practical than problem solving fromscratch [3]. However, this approach, also referred to as case
based reasoning, requires a complete case base and an appro-priate retrieval system, which has not been reported for die and
mold cost estimation so far.
In the analytical cost estimation, the entire manufacturing
activity is decomposed into elementary tasks, and each task isassociated with an empirical equation to calculate the manu-
facturing cost. For example, a common equation for machiningcost is
Machining cost= (cutting length / feed per minute)
machine operation cost. (1)
Wilson (quoted in [4, Chap. 6, p. 121]) suggested a mathematical
model for incorporating a geometric complexity factor in turning
and milling operations, given by:
Complexity factor I=
Ni=1
log2
di
ti
, (2)
where
di = ith dimension of feature
ti = corresponding dimensional tolerance
N= total number of dimensions .
This is explained with the help of an example later.
Another method called activity based costing (ABC) involves
applying the analytical method to all steps in manufacturing
a given product, to estimate the resources (material, labor and en-ergy) involved in each step. Such a detailed approach for various
processes, including casting has been developed by Creese [5]. In
tool rooms, this approach is used in the case of dies with complex
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cavity geometry. The sources of mold cost can be divided intothree categories: mold base cost, functional elements (core, cav-
ity inserts) cost and secondary elements cost. In each category,the time needed to obtain the desired geometry by machining isconsidered as a reference for costing [4]. As can be expected, es-
tablishing and validating the costing equation, as well as using itin practice, are cumbersome tasks.
In the feature based method, mold geometric features (cylin-
der, slot, hole, rib, etc.) are used as the cost drivers. The die
manufacturing cost is then estimated using either empirical equa-
tions or tools such as knowledge-based systems and artificial
neural networks. Chen and Liu [6] used the feature recognitionmethod to evaluate a new injection molded product design for its
cost effectiveness. They assumed that a product is an aggregationof a set of features and feature relationships. These feature rela-
tionships were mapped to convert a part feature into mold related
cost evaluations. Chin and Wong [7] used decision tables linked
to a knowledge base to estimate injection mold cost.In the parametric cost estimation, technical, physical or func-
tional parameters are used as basis for cost evaluation. Thismethod allows one to proceed from technical values character-
izing the product (available with design engineers) to economic
data. Sundaram and Maslekar [8] used regression model ap-proach in injection mold cost estimation. Lowe and Walshe [9]
used labor involvement in injection mold making as a reference;mold cost was estimated using linear regression analysis.
To summarize, cost similarity and cost functions (cost fac-
tors) are the two sets of methods for estimating the mold cost.
In the first set, similarity between a new mold and a previ-ous mold developed in the tool rooms is used as a reference.
Intuitive and analogical methods fall under this category. In thewidely used intuitive method, the cost appraiser may not be in
a position to identify all the risk factors and to quantify many ofthem. The analogical method can be successfully used for esti-
mating the cost of die bases and other secondary elements where
grouping is much easier. However, in the case of functional elem-
ents (core and cavity), grouping becomes a difficult task as theirgeometry, machining sequence and tolerance greatly vary with
product design.In the second set of methods, the dependency between the
mold cost and its drivers are expressed in mathematical func-
tions. Analytical method, activity based costing, feature based
method and parametric costing methods falls under this cate-
gory. While analytical methods are well established for esti-mating the machining cost of simple parts, they are difficult to
apply in die and mold manufacturing because of their geometric
complexity. Similarly, feature based cost estimation is difficult
to apply because the current feature recognition and classifi-cation algorithms cannot handle freeform surfaces present in
most of the dies and molds, and other computational techniqueslike knowledge-based systems, fuzzy logic and artificial neural
networks may be required to establish the cost relations. Fur-
ther, these techniques may not be able to consider the impact
of assembly restrictions, surface finish requirements, mold trials
and other factors. The parametric costing method functions like
a black box, by correlating the total cost of mold with a limited
number of design parameters, and it is difficult to justify or ex-plain the results.
Menges and Mohren [10] developed an integrated approach
for injection mold cost estimation, in which similar injectionmolds and structural components of the same kind are grouped
together and a cost function for each group is determined. Thecost components are grouped into cavity, mold base, basic func-
tional elements and special functional elements. Machining cost
for cavity and EDM electrodes is driven by machining time and
hourly charges adjusted by factors like machining procedure,cavity surface, parting line, surface quality, fixed cores, toler-
ances, degree of difficulty and number of cavities. The moldbases are assumed to be standard components. Cost of basic
functional elements like sprue, runner systems, cooling systemsand ejector systems are estimated on a case to case basis. The
cost of special functional elements like side cores, three-plate
mold, side cams and unscrewing devices is determined based
on actual expenses. One of the limitations is that the machiningtime estimate based on mean cavity depth may not give accurate
results in case of complex shaped molds that require differentmodes of machining like roughing, finishing and leftover mate-
rial machining, due to cutting tool size and geometry constraints,
orientations and settings. Secondly, the work does not appear toconsider machining cost for secondary surfaces (particularly in
case of built-in type cavities or cores), cost implications of moldmaterial (which directly affects cutting tool selection and ma-
chining time), secondary operations on standard mold bases (to
accommodate cavities, side cores and accessories, special ejector
mechanisms and hot runners etc.), and some cushion in cost esti-mation to take care of additional work during final machining of
mating parts.This approach uses more than 1520 analytical models with
an average 58 variables, which need to be statistically estab-
lished, and offers research opportunities.
In general, all of above approaches give relatively accu-
rate estimates only when tool rooms are involved in develop-
ing a single type of mold (such as injection molds or pressuredie casting dies). Die and mold manufacturing is still regarded
as skill and experience oriented manufacturing, and moreoverit is not repetitive in nature. Thus there is a need to develop
a generic die and mold cost estimation model that can be eas-
ily implemented for different types of molds and complexity,
and is also flexible to accommodate the decisions of the cost
appraiser. We propose a cost model to meet the above require-ments, based on the notion of cost drivers and cost modifiers.
Cost drivers depend on geometry and machining time. Cost mod-
ifiers depend on complexity, and can be customized using a qual-
ity function deployment approach, which is also discussed inthis paper.
3 Framework for die and mold cost estimation
The cost components of a typical injection molded automo-
tive part (assuming a die life of 250,000 parts) are given in
Fig. 2 [11]. It shows that mold cost (41%) has a much larger
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Fig. 2. Cost break up of a typical injection molded automotive part [11]
share of total cost and therefore must be estimated accurately.Molds for other applications (pressure die casting, forging,
sheet metal tools, etc.) also reflect a similar breakup. The moldcost comprises mold material, mold design and manufacturing.
Among these mold-manufacturing costs represents the largest
share and is the focus of our work. The structure of the pro-posed mold cost estimation model is shown in Fig. 3. In this
approach, all geometric features are mapped to machining fea-tures, which are used as cost drivers and their cost is obtained
by the analytical costing method. Other factors affecting the
complexity of the die and mold are considered as cost modi-
fiers. Hereafter, the term mold will be used to represent both dieand mold.
3.1 Cost drivers: core and cavity features
In feature based design, a part is constructed, edited and ma-
nipulated in terms of geometric features (such as hole, slot,
rib and boss) with certain spatial and functional relationships.
The part features are used for generating mold cavity features;Table 1 shows the feature mapping between part and mold. The
mold features are analyzed to identify the geometric dimen-sions, manufacturing processes and relative manufacturing cost.
Essentially, the size and shape complexity of mold cavity fea-
tures, which in turn influence the selection of the manufacturing
Table 1. Part to tooling feature mapping and relative cost
Part Round boss Round hole Outside Tapered Square hole Square boss L-shape Straight ribs Inclined BSpline/features Concave boss boss ribs NURBS
Mold Round hole Round pin Convex Tapered Square Square L-shape Grooves/ Inclined BSpline/ features cavity hole protrusion cavity cavity channels groove NURBS
Dimen- D x L D x L R x L x W D/d x L L x B x W L x B x W L /l x W L x B x W L /l x W Cutting areasions (D x L)
Mfg Milling/ Turning/ Milling EDM Milling Milling + Milling + Milling + EDM 3D Milling +Process EDM Drilling EDM EDM EDM EDM
Mfg method 1D 1D 2D 2D 2D 2D + 1D 2D + 1D 2D + 1D 2D 3D / 5DRelative Cost 1 1 3 4 2 8 6 4 8 10
method, act as cost drivers. The manufacturing methods 1D,2D, etc., represent the simultaneous movement of tool or work
piece with respect to axis X, Y, Z, a, b and c, to get the de-sired geometry. The relative cost for feature manufacturing (ba-sic mold cost) is proposed based on our experience. This is
useful when sufficient mold design and cost data are not avail-able. More precise cost estimation can be assured by integrat-
ing analytical costing methods with machined features in later
stages.
The manufacturing cost of mold geometry can be calculated
by Eq. 1 using predetermined machining parameters like feed per
minute (S) and machine hour rate. The summation of machiningcost of all features gives the basic mold cost:
Basic mold cost Cf =
nf=1
If
Lf
S
Mf
, (3)
where,
Lf = Total cutting length of feature ( f= 1 to n)
S= Corresponding feed(mm/min)
Mf = Corresponding machine minute rate(hour rate /60)
If =Machining complexity factor I
n = number of features .
While calculating the machining complexity factor for cost
estimation purposes, it is not necessary to consider all dimen-sions of a feature (the process engineer will select the manu-
facturing process and corresponding machine considering thegeometry as well as tolerance of primary dimension). The ma-
chine hour rate already considers these effects. There are other
factors like the number of settings, number of tooling and their
sequence, which are again dependent on geometric complexity
(number of surfaces and their orientation and special relation-ships). We therefore modified Eq. 1 by introducing a machining
process constant K. The value of K varies from 0.05 for plain
turning to 0.5 for EDM and machine polishing processes.
Thus machining complexity factor of a feature is given by:
If = Klog2
di
ti
. (4)
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Fig. 3. Structure of the proposed die and mold cost estimation model
For example, consider a circular hole feature with diameter
20+0.018 mm and depth 160.010 mm. In this case, diameter 20
is a primary dimension and tolerance 18 m can be achieved
by the reaming operation. Therefore, it becomes necessary to
consider only the depth that is, 160.010 . Reaming operation is
normally performed in either CNC vertical machining center or
a jig-boring machine. The number of settings is one, and the
number of tooling is four (center drill, pilot drill, final drill and
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Die construction complexity Injection molds Pressure die casting dies
Side core Extra cavity Side cores Extra cavityc cv c cv
Uncomplicated parts without cores 0 2.5 % 0 5%
Parts with some complexity, often without 35% 5.0 % 510% 8%cores or with few cores
Complex parts, often with one or several 510% 7.5% 1015% 11%cores that move in the same direction
Very complex parts with cores in several 1025% 10% 1530% 15%directions
Table 2. Cost impact of side core com-plexity (c)
machine reamer). Therefore, the machining process constant is
considered as 0.2. Hence the machining complexity of the abovefeature is given by:
If = 0.2log2
16
0.020
= 1.93 .
3.2 Cost modifiers: Die complexity factors
In die and mold manufacturing, there are many die complexity
factors that have a significant impact on the total cost and are
considered as cost modifiers. These include parting surface com-plexity, presence of side cores, surface finish and texture, ejector
mechanism and die material. Their values, established from our
experience, are given in Tables 24, as a percentage of the ba-
sic mold cost (derived from Eq. 3). These are explained in detailhere.
3.2.1 Parting surface complexity
Selection of the most appropriate parting surface is an im-
portant activity in die and mold design. Many researchers
have reported different algorithms to identify a parting sur-
face considering the ejection of part from die cavity, ease of
Table 3. Cost impact of surface finishing (p)
Type of surface finish Cost modifier p
Surface finish Ra > 0.8m 510%Surface finish Ra < 0.8m 1018%Surface texturing by EDM 1525%Surface texturing by etching 2035%
Table 4. Cost impact of ejector mechanism (e)
Type of ejectors Cost modifier e
Round ejector pins / blades 15%Stripper plate, sleeve ejections 5%Self screwing mechanism 510%Hydraulic-pneumatic ejectors 1015%
manufacturability and aesthetic issues. A complex parting sig-
nificantly increases the manufacturing cost due to increase inmachining complexity (because of cutting tool geometry con-
straints) and die assembly time. A non-planar parting surface
makes it difficult to match the two halves. Often it results in
re-machining, which is not quantifiable by feature based ap-proach. To consider these uncertainties, die parting surface
complexity is divided into three levels: straight, stepped andfreeform parting surfaces. Straight parting surface will not im-
pose any additional cost, however the cost implications of
steeped and freeform parting surface will 1020% and 2040%, respectively. This can also be customized as discussed in
a later section.
3.2.2 Presence of side cores
The product geometry may comprise a number of undercuts tothe line of draw, hindering its removal from the die and mold.
This is overcome by the use of side cores, which slide in such
a way that they get disengaged from the molded part before its
ejection. Side cores need secondary elements like guide ways,
cams and hydraulic-pneumatic actuators, which impose an addi-
tional cost. If product geometry calls for a number of side coresthat are actuated in different directions, then die size and cost
will increase significantly. Aggravated by additional die coolingarrangements, increased mold assembly time and finish machin-ing during assembly, which may not be easily quantifiable. While
the cost of side cores machining is already considered in costdrivers, their influence on over all die complexity due to addi-
tional accessories, and secondary machining is considered here.The corresponding values for this cost modifier (c) are given in
Table 2 based on our experience.
3.2.3 Surface finish and texture
The die surface is usually polished to obtain surface roughness
Ra from 0.2 to 0.8m. Some surface textures may be added
to injection-molded parts to increase the aesthetic look or some
functional requirement. This requires specialized processes like
EDM texturing, photo etching and surface treatment, increasingthe toolmakers job. Therefore, polishing and texturing impose
additional cost, and the values for this cost modifier (p) are
listed in Table 3 based on our experience.
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3.2.4 Ejection mechanism
The mechanism for ejecting a part from its mold or die may com-
prise a simple ejector pin or cam operated mechanism, or a com-plex hydraulic-pneumatic actuator. Construction of the ejector
mechanism depends on the part geometry and the desired rate of
production. In addition, ejector design may lead to a larger die
size to accommodate the sliders, cams, actuators, etc. The ejec-
tor materials are usually of special grade, requiring hardening
and nitriding treatments. Therefore, the ejector mechanism addsto the total cost depending on its type. The values for this cost
modifier (e) are given in Table 4.
3.2.5 Die and mold material
The die and mold material should have good mechanical proper-
ties like high hardness, low thermal distortion, high compressivestrength and manufacturability. Commonly used tool steels for
injection molds and pressure die casting dies include P20, P18,
EN-24, A3, D1, D2, H11 and H13, which are more expensive
than general steels. The die material cost is directly based on thevolume of die inserts (considered in the total cost model). The
die material also affects the feature manufacturing cost, becauseof its impact on cutting tool life. A recent development is high
speed machining of hardened die steel, which shows significant
improvement in accuracy and surface finish. Based on an aver-
age of ten case studies carried out at our center, the die material
factor (m) can increase the basic mold cost by 210%, for die
materials ranging from carbon steel to hot die steel.
3.3 Total cost model
The total cost model for die or mold manufacturing is determinedby taking the basic feature machining cost and modifying it using
various die complexity parameters, then adding the cost of sec-
ondary elements and other activities.
Total mold cost= die material cost
+ (basic mold cost cost modifiers number of cavities)
+ (Standard mold base cost assembly factor)
+ secondary element cost+ tool design and tryout charges.
Mc =Cm +
n
f=1
If
Lf
Sf
Mf
1+ps+c+p+e+m
100 nc
+Cb
1+
a
100
+Cs +Cd (5)
where,
Cm =Die material cost
nc =Number of cavities
Cb = Standard mold base cost
Cs =Secondary element cost including ejector, sprue, guides
and screws
Cd=Tool design and tryout cost= 1525% of total mold
manufacturing cost
ps =Cost modifier due to parting surface complexity
c =Cost modifier due to side cores
p =Cost modifiers due to polishing and surface texturing
e =Cost modifiers due to ejector mechanism
m =Cost modifiers due to material machining characteristics
a =Cost modifiers for assembly preparation.
The factor a includes material handling and additional laborcost, and varies from 520% depending on the die size.
4 Establishing the cost modifiers
As seen from Tables 24, the impact of various factors on the
total cost of a die or mold cost is significant. While the values
given in the above tables are based on our experience, they can-not be justified in other tool rooms, unless they have a large casebase to verify the same. The cost modifiers must therefore be
customized for an individual tool room.
One way to customize the cost modifiers is by using multiple
regression analysis. This involves collecting historical data and
establishing the regression coefficient or cost estimating rela-
tionships (CERs). However, the CERs established in commercialtool rooms may not simulate the real situation, since such tool
rooms manufacture a large variety of dies and molds, and a hugeamount of historical data would be required for computation.
We propose another approach, based on quality function de-
ployment (QFD) for establishing the cost modifiers, to overcome
the above limitation.This QFD-based cost model is project specific, and estab-
lishes the cost factors by considering the different tooling param-eters. The user has to assess the impact of tooling parameters
(parting surface complexity, surface finish, etc.) by considering
basic mold cost as a reference. This improves the accuracy of totalcost estimation. Table 5 explains the tooling parameters and their
associated cost factors considered in developing the QFD-basedcost model. The steps involved in the methodology are as follows:
1. Identify major tooling parameters other than basic die andmold feature manufacturing.
2. Categorize the tooling parameters into different complexity
levels (columns of QFD).3. Identify cost elements other than basic mold manufacturing
cost (rows of QFD).
4. Represent the importance of these cost elements in percent-age of basic mold cost. For example, parting surface machin-
ing cost is about 10% of basic mold cost, and hence 0.1 isused as cost appraisers preference.
5. Develop the relationship matrix considering the complexity,
using 19 scale (1 =weak, 3=medium, 9= strong)
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Table 5. Major tooling parameters and associated cost factors
Sr. No. Tooling parameter Cost factors
1 Parting surface complexity Parting surface machining costDie assembly costRe-machining cost
2 Presence of side core Mold housing machining costAccessories preparation costDie assembly cost
3 Surface texture and finish Finish machining / polishing costSurface treatment cost
4 Ejector mechanism Ejector material / std costMachining & assembly charges
5 Die material condition Heat treatment costCutting tool cost
6. Construct the correlation matrix using 0.11.0 scale (0.1 =
weak, 0.3=medium, 0.9= strong)7. Normalize the relationship matrix using the Wasserman
method. The coefficient of the normalized matrix is given bythe following equation [12]:
rnormi.j =
mk=1
(ri.j k.j)
mj=1
mk=1
(ri.j j.k)
, (6)
where
ri.j = coefficient of relationship matrix
j.k= coefficient of correlation matrix .
8. Calculate the technical importance of each tooling parameter.
9. The technical importance values can be used as respectivecost modifiers.
The entire methodology for die and mold cost estimation is
illustrated with an industrial example in Sect. 5.
5 Industrial example
Figure 4 shows an aluminum part used in ceiling fans, along with
the corresponding die inserts. The fan component is produced
using cold chamber pressure die casting process. The die design
Fig. 4. Pressure diecast component and die inserts
and development was relatively difficult as the part consists ofa number of small geometric features and split parting surface.
A combination of CNC and EDM processes are used to manu-facture core and cavity die inserts in H13 material. Mold bases,ejectors and screws are purchased from standard vendors.
5.1 Basic mold manufacturing cost
A CAD model of the casting was used as input to design the die.To estimate the basic mold cost, the mold machining features andthe corresponding processes were first identified. Then the fea-
ture machining cost was estimated using Eq. 3. The feature and
its critical dimensions di (ith dimension of feature) and corres-
ponding dimensional tolerance ti (dimensional tolerance of ith
dimension) were considered in calculating the complexity factor.
The results are shown in Table 6. The following rates were used(in Indian Rupees; 1 INR US$ 0.02):
Turning operation: Mf = INR 400/hr (CNC lathe)
3D Milling operation: Mf = INR 700/hr
(CNC machining center)
2D milling operation: Mf = INR 120/hr (conventional milling)
EDM operations: Mf = INR 250/hr
Wire cut EDM: Mf = INR 400/hr
Jig boring: Mf = INR 300/hr .
5.2 Cost modifiers
The main complexity characteristics of the die considered in this
example are as follows:
Straight parting surface (simple)
Circular cavity split on both sides (chances of mismatching)
12 ejector pins (diameter minimum 3 mm, maximum 8 mm) Die material H13 (needs hardening and tempering, hard to
machine)
Surface finish Ra < 0.4m (needs polishing) Number of side cores: Nil
Number of core pins: 12+1 (alignment is critical).
The QFD model was developed as discussed in Sect. 4. The
eight cost elements are represented in the first column of the
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Table 6. Basic mold cost (using Eq. 3)
Tooling Mold features Num. of Machining Cutting Complexity Mfgelement (Cost drivers) features method Lf/Sf factor (If) cost
Cavity Circular cavity (female) 1 CNC Turning 22400/160 1.3 1213
Circular hole for core pin 3 Jig boring 3000/100 1.4 630
Central hole for core insert 1 CNC Turning 2000/160 1.4 116
Gates (feeding+overflow) 7 EDM 0.9/0.01 1.5 984
Grove (circular) 1 Turning 1800/100 1.8 216
Core Circular core (male) 1 Turning 25820/120 1.5 2151
Central stepped hole 1 Turning 1200/80 1.2 120
Ribs 6 CNC Milling 300/60 1.3 455
Blind holes 12 Milling 100/60 1.2 280
Ribs (small) 12 EDM 3/0.01 1.0 15000
Land 1.6 mm depth 6 EDM 1.6/0.01 1.0 4000
Ejector pin hole 18 Jig boring (reaming) 100/20 1.4 630
Runner 1 CNC Milling 3923/180 1.2 305
Overflows pocket 6 Milling 3056/150 1.0 1426
Core pins Circular rods 6 CNC Turning 720/60 1.4 672
Cavity pins Circular rods 6 CNC Turning 745/60 1.4 695
Actual manufacturing cost of functional parts (core, cavity and core pins/inserts) 28893
Miscellaneous operations (blank preparation, reference plane machining, surface grinding) 5778= 20% of actual machining cost
Basic Mold Cost 34671
Note: Cutting length Lf for different operations are given by the following:Turning = Length of turningnumber of cutsMilling = Length of feature (width/step over) number of cutJig boring= Feed length (depth of bore)EDM= depth of pocket to be finishedWire cut EDM= total travel length
QFD model shown in Table 7. The decisions of the cost ap-praiser are represented in the second column, in terms of per-
centages of basic mold cost. For example, cost appraisers as-sessment for parting plane and associated machining is 10%
of basic mold cost; and for housing machining cost to accom-
modate functional elements (core and cavity) it is 9%. The
die design complexity was analyzed and the cost implicationsof the individual parameter were rated using the 19 scale to
complete the relationship matrix. To keep the calculations sim-ple, the correlation matrix was not considered. Table 8 repre-
sents the normalized relationship matrix of QFD. The different
Table 7. QFD before normalization
Cost modifier
Cost elements Percentage Straight Ejector Few core Surface Die materialcost w.r.t. parting design pins in finish condition
Basic mold cost surface 12 pins both halves Ra < 0.8
Parting plane machining 0.10 1 3 3 1Re-machining 0.08 3 3 3 9Housing machining 0.09 1 9 3 1Polishing 0.15 1 3 9 9Heat treatment 0.06 9 9 3 9Cutting tool 0.05 1 3 3 9Die assembly 0.10 1 9 3 3Mold trial & rectification 0.07 1 3 3
cost modifiers were calculated by adding the coefficients of therespective column.
The impact of various tooling parameters (cost modifiers) ontotal mold cost is given below:
Parting surface factor (ps)= 5.8%
Ejector mechanism factor (e)= 18.4%
Core pins factor (c)= 13.6%
Polishing factor (p)= 14.1%
Die material factor (m)= 17.8% .
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Table 8. QFD after normalization
Cost modifier
Cost elements Percentage Straight Ejector Few core Surface Die materialcost w.r.t. parting design pins in finish conditionBasic mold cost surface 12 pins both halves Ra < 0.8
Parting plane machining 0.10 0.125 0.375 0.375 0.125Re-machining 0.08 0.166 0.166 0.166 0.500Housing machining 0.09 0.071 0.642 0.214 0.071
Polishing 0.15 0.045 0.136 0.409 0.409Heat treatment 0.06 0.3 0.3 0.1 0.3Cutting tool 0.05 0.062 0.187 0.187 0.562Die assembly 0.10 0.062 0.562 0.187 0.187Mold trial & rectification 0.07 0.142 0.428 0.428
Cost importance 0.058 0.184 0.136 0.141 0.178
5.3 Total mold cost
The calculations of total mold cost are given below (in Indian
Rupees; 1 INR US$ 0.02):
1. Die material cost: Cm = INR 26325 (approximately 135 kg
@ INR 195/kg)
2. Basic mold manufacturing cost= INR 34671 (see Table 6)
3. Mold base cost: Cb = INR 58000 (mold base set was pur-chased from vendors). Assume mold base preparation cost
a = 5% of base cost4. Secondary elements cost = Cs (screws and ejectors) =
INR 10200
5. Tool design charge Cd 15% of basic manufacturing cost =
INR (26325+34671+58000+10200)0.15 = INR 19379.
Therefore, total mold cost using Eq. 5 is given by:
Mc =26325+34671
1+
5.8+18.4+13.6+14.1+17.8
100
+58000
1+
5
100
+10200+19379
=26325+58836+60900+10200+19376
= INR 175,637 .
6 Validation of the cost model
The cost model developed in this work was validated by usingit for 13 industrial cases, including 7 injection molds, 3 pres-
sure die casting dies, 2 wax molds and a compression mold. All
these were developed at the Central Mechanical Engineering Re-
search Institute in India in the last four years. The methodology
followed in each cases included:
1. Identification of part features.
2. Feature mapping: converting part features into mold features
and then machining features.
3. Basic mold cost estimation using Eq. 3.
4. Customization of cost modifiers using QFD model as dis-cussed in Sect. 4.
5. Estimation of mold base cost (Cb), secondary elements cost
(Cs) and core and cavity material cost (Cm ).
6. Final die and mold cost estimation using Eq. 5.
7. Listing quoted, actual and estimated costs (Table 9). The
quoted cost is based on the tool designers experience. Theactual cost is accounted from operators machine logbook
records and manpower schedule. The estimated cost is deter-mined from the cost model.
8. Calculation of deviations for comparative evaluation.
The cost deviations of the two methods, intuitive method
(used for quotation purpose) and the proposed cost model, werecalculated and compared (Fig. 5). The average deviation of es-
timated cost from actual cost is found to be 0.4% for the pro-posed cost model compared to 2.5% for the intuitive method.
The maximum deviations are 2.5% for the proposed model com-pared to 16% for the intuitive method. An additional exercise
was to study the effect of overall complexity of the molds
on cost deviation. For this purpose, the examples were sorted
in the ascending order of their overall complexity as follows:
Case numbers:12341112891065713
Simple Complex
Fig.5. Cost deviation comparison
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Table 9. Results for different case studies (costs in India Rupees)
Type of die / Case Product / die Quoted Actual cost Cost Percentage of deviationmold Description price (accounted) model
estimateQ A E (1Q/A)100 (1E/A)100
Injection 1 4-cavity IM for 50,000 48,300 46,520 3.51 3.68molds (IM) terminal block 1
2 4-cavity IM for 50,000 46,234 46,400 8.14 0.35
terminal block 23 4-cavity IM for 50,000 53,000 52,700 5.66 0.56terminal block 3
4 2-cavity IM for 50,000 52,800 48,830 5.30 7.51terminal block 4
5 44-cavity IM for 2,00,000 1,93,500 1,82,000 3.35 5.94cable ties (150I)
6 36-cavity IM for 2,00,000 1,86,000 1,84,650 7.52 0.72cable ties (200I)
7 Single cavity IM for 2,50,000 2,40,300 2,38,000 4.03 0.95pump impeller
Pressure die 8 Single cavity PDC die 1,30,000 1,25,000 1,25,500 4.00 0.4casting (PDC) dies for fan cover type-I
9 Single cavity PDC die 1,35,000 1,28,450 1,32,400 5.09 3.07for fan cover type 2
10 Single cavity PDC die 1,70,000 1,74,000 1,75,637 2.29 0.94for top cover
Wax injection 11 2-cavity wax mold 35,000 33,650 36,200 4.01 7.57molds for rear sight
12 Single cavity wax 45,000 46,100 44,890 2.38 2.62mold for bracket
Rubber 13 Split mold for face 2,30,000 1,98,000 2,06,600 16.16 4.34compression piece of rubbermold oxygen mask
Mean deviation 2.47 0.40
It is seen from Fig. 5 that the proposed model gives better
results than the intuitive method for complex molds, in which ac-curate cost estimations are more important owing to the higher
costs involved. The proposed cost model also appears to be more
flexible, and can be easily customized to individual tool room
practices by establishing their own ratings for cost modifiers.
7 Conclusion
Die and mold development procedure varies from part to part and
is not very well documented. The conventional cost estimation
methods depend on the experience of the toolmaker and may not
yield realistic estimates, especially when die complexity is high.In this work, feature based approach, activity based costing and
parametric costing methods were integrated to develop a hybrid
die and mold cost estimation model. This cost model is flexibleand project specific, yet easy to apply. A quality function deploy-
ment approach has been proposed for customizing the tooling
cost modifiers. This enables incorporating the experience of the
cost appraiser as well project-specific complexity indicators. The
proposed cost model has been validated on 13 industrial exam-ples, including injection molds and pressure die casting dies. The
average deviation was only 0.40% and the maximum deviation
was 7.6%.
The proposed cost model forces a systematic approach,
which may be difficult to implement in smaller tool rooms. Sec-ondly, feature identification and complexity rating for customiz-
ing the cost modifiers require some expertise and experience.
Integrating a computerized database of previous cases, along
with automated feature recognition can overcome the above lim-itations and also enhance the efficiency of the proposed cost
model. This is presently being investigated.
Acknowledgement The authors would like to acknowledge the Tool andGauge Manufacturers Association (TAGMA), Mumbai, India for sharingthe information on status of Indian die and mold manufacturing industries.The cooperation of the staff of Manufacturing Technology Group, Cen-tral Mechanical Engineering Research Institute Durgapur in die and molddevelopment and establishing the machining process constant is also ac-knowledged.
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