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International Journal Of Advancement In Engineering Technology, Management and
Applied Science (IJAETMAS)
ISSN: 2349-3224 || www.ijaetmas.com || Volume 05 - Issue 01 || May-2017 || PP. 202-212
www.ijaetmas.com Page 202
Parametric study of Fused Deposition Modelling by
Design of Experiments Shwetha K
1, H N Narasimha Murthy
2, K V S Rajeswara Rao
3, N S Narahari
4, Rohit
Agarwal, Rahul Singh6
2,5,6 Department of Mechanical Engineering, R.V.College of Engineering, Benguluru, India,
1,3,4 Department of Industrial Engineering and Management, R.V.College of Engineering, Benguluru, India.
Abstract— Fused deposition modelling is a fast-growing rapid prototyping technology due to its ability to build functional
parts with complex geometry in reasonable build time. Mechanical strength and functionality of built parts are dependent on
several process variables. In this research layer thickness, shell thickness and infill density were investigated for their effects
on tensile, compression and flexural strengths of the Acrylonitrile Butadiene Styrene specimens. Shell thickness and layer
thickness influenced tensile strength and flexural strength respectively. Multiple Regressions was used to predict the
strengths of the fused deposition model specimens with good accuracy. As per the grey relational grade, tensile,
compression and flexural strengths are maximized at layer thickness of 0.1 mm, shell thickness of 1.5 mm and infill density
of 40 %. While Tensile strength is maximized at lower layer thickness and flexural strength is maximised at higher shell
thickness and infill density.
Keywords— Fused Deposition Modelling; Multiple Regression; Grey Relational Grade; Rapid Prototyping;
Surface Roughness;
I. INTRODUCTION
Rapid prototyping (RP) has been undergoing great advances in the last few years. RP
enables building parts with complex geometries in short time and at low costs. Its main
advantages lie in the ease of generation of a 3D prototype from a concept along with
simplified manufacturing and assembly tasks [1]. It enables providing functional assemblies
by consolidating subassemblies into single unit at the computer aided design (CAD) stage
and thus reduces part counts, handling time, storage requirement and avoids mating and fit
problems [2-3].
Yonghua Chen et.al [4] reported analysis of pin joint designs using Fused Deposition
Modelling (FDM). Drum shaped pin joint design provided minimum joint clearance in layer-
based fabrication without weakening the joint strength compared to the traditional cylindrical
pin joint design. Alberto Boschetto et.al [5] predicted surface roughness after barrel finishing
operation using layer thickness, deposition angle and the material removed during barrel
finishing operation.
Peng An Hua et.al [6] reported that part errors in FDM are due to dimension error, shape
error and roughness of surface including deformation and stair-stepping effect. With increase
in slicing thickness, deformation decreases and stair stepping errors increase. Galantuccia L.
M. et.al [7] reported influence of raster width, slice height and tip size on the dimensional
accuracy of FDM specimens. The authors observed that the deviation from the ideal
dimensions was encountered on the first layer material deposition due to the material
adhesion problems which had cascading effect on the increase in height of other layers. Nur
Saaidah Abu Bakar et.al [8 -9] reported optimisation of raster angle, tool path, slice thickness,
build orientation, and deposition speed to achieve minimum deviation in the specimen
dimensions. Based on parametric studies Jose Stockler Canabrava Filho et.al [10] observed
that the specimens whose layers were superimposed in the direction perpendicular to the
thickness had twice the strength compared to those in which layers are superimposed
International Journal Of Advancement In Engineering Technology, Management and
Applied Science (IJAETMAS)
ISSN: 2349-3224 || www.ijaetmas.com || Volume 05 - Issue 01 || May-2017 || PP. 202-212
www.ijaetmas.com Page 203
lengthwise. Breaking strain of the specimens with layers in the longitudinal direction were
approximately 20% less than that of the specimens with layers in the thickness direction.
Anoop Kumar Sood et.al [11] studied the effect of increasing number of layers in building
the FDM specimens. The authors observed that increase in number of layers increased the
temperature gradient due to increased diffusion between adjacent rasters leading to
improvement in strength. But it causes distortion within the layers or between the layers.
Dinesh Kumar S et.al [12] reported that layer thickness, raster angle, raster width and shell
thickness have effect on the plastic deformation of the FDM parts. Anoop Kumar Sood et.al
[13, 14] observed that layer thickness, part build orientation, raster angle, raster width and air
gap influenced the responses in non-linearly. The model analysed using Artificial Neural
Network showed the errors between the predicted and the observed between 0 % and 3.5 %.
Dietmar Drummer et.al [15] reported influence of layer thickness and infill density on the
strength and other properties. Cany Mendonsa et.al [16] demonstrated the influence of print
speed, Layer thickness and Infill density on the build time and optimization of FDM parts.
The authors observed that the build time for a given print can be reduced by decreasing the
layer thickness and reducing the infill density.
Review of Literature [1-16] indicated influence of layer thickness, part build orientation,
raster angle, raster width and air gap on the FDM specimens. Newer versions of FDM
machines have inbuilt provision to optimize the material consumption and the properties of
FDM components by changing infill density, shell thickness and layer thickness, which
leaves scope for detailed investigation and parametric optimization of newer FDM specimens.
The main objective of this research was to study the influence of infill density, shell thickness
and layer thickness on the tensile, compression and flexural properties of FDM specimens
fabricated using ABS based on Orthogonal Array Experimentation, ANOVA, Grey
Relational Analysis and Regression Analysis.
II. EXPERIMENTAL
A. Parameters and Levels for Design of Experiments
Fused Deposition Modelling Rapid Prototyping Machine, 3D Protomaker IMEC
Technologies, Bangalore was studied for the parametric optimisation. Specifications of the
machine are highlighted in Table 1. Three build parameters namely layer thickness, shell
thickness and Infill density, each at three levels were selected for the investigation. Layer
thickness or layer height directly influences the quality of final print. The default layer
thickness of 0.2 mm in the machine gives decent prints. For high quality prints layer
thickness of 0.1 mm may be used at the cost of building time which is twice that for 0.2 mm.
Shell thickness refers to the thickness of outside walls. In case of a cube, shell thickness
controls the front, back and side thicknesses. Normal thickness of 0.8 mm gives good results.
But depending on the size of the specimen it may be lowered. Infill density is the amount of
material deposited within the specimen. It is generally expressed in percentage. While infill
density influences the weight and material content it also adversely influences the mechanical
properties such as toughness. Acrylonitrile Butadiene Styrene (ABS) SD-0150 concentration
range weight percentage of 96.5 - 99, specific gravity of 1.04 g / cm3 from Samsung Cheil
Industries Ltd was used for fabricating the FDM specimens.
International Journal Of Advancement In Engineering Technology, Management and
Applied Science (IJAETMAS)
ISSN: 2349-3224 || www.ijaetmas.com || Volume 05 - Issue 01 || May-2017 || PP. 202-212
www.ijaetmas.com Page 204
TABLE 1
SPECIFICATIONS OF 3D PROTOMAKER IMEC TECHNOLOGIES, BANGALORE
Parameter Details Parameter Details
Maximum build size 250 x 250 x 200 mm3 Print Accuracy 50 micron
Build material PLA and ABS Power requirements 12V DC, 15A
Filament Diameter 1.75 mm and 3 mm CAD input data file format
supported
STL
Layer thickness 0.15 to 0.3 mm 3D printing software Pronterface with Slic3r
Printing modes Solid, honeycomb and hollow Operating system Windows XP, Windows
7
Printing temperature 170 to 200 C for PLA 200 to
240 C for ABS
Power consumption 180 W
Tensile, compression and flexural specimens as per ASTM D638, ASTM D790 and ASTM
D695 were fabricated based on the CAD models using the FDM machine (Fig. 1). The tests
were conducted on Universal Testing Machine (Fig. 2) at cross head speed of 1 mm/s for
compression and flexural tests and 2 mm/s for tensile tests. The experiments were conducted
as per L9 Orthogonal Array lay-out.
Fig. 1 FDM-3D Protomaker
Fig. 2 UTM- Universal testing Machine (UTM)
International Journal Of Advancement In Engineering Technology, Management and
Applied Science (IJAETMAS)
ISSN: 2349-3224 || www.ijaetmas.com || Volume 05 - Issue 01 || May-2017 || PP. 202-212
www.ijaetmas.com Page 205
III. EXPERIMENTAL RESULTS
The experimental responses for the nine treatment combinations are presented in Table 2.
TABLE 2
L9 ORTHOGONAL ARRAY LAYOUT
Sl.
No.
Experimental Factors Measured Experimental Responses, MPa
Layer
Thickness,
mm
Shell Thickness,
mm
Infill
Density, % UTS
Compression
Strength Flexural Strength
1. 0.1 0.5 20 26.954 19.981 119.07
2. 0.1 1 30 35.493 21.651 152.74
3. 0.1 1.5 40 34.090 48.150 146.22
4. 0.2 0.5 30 29.381 27.771 108.71
5. 0.2 1 40 36.925 31.443 114.97
6. 0.2 1.5 20 26.046 29.424 118.45
7. 0.3 0.5 40 33.175 18.831 112.47
8. 0.3 1 20 37.925 32.372 89.961
9. 0.3 1.5 30 28.174 43.223 135.05
A. Analysis of Variance (ANOVA) of the Experimental Responses
ANOVA was performed to investigate the influence of the parameters at 95% confidence
level using MINITAB 16 version. The assessment was made using F and P distributions.
ANOVA is summarized in Table 3 for tensile, compression and flexural strength of the FDM
specimens.
TABLE 3
ANALYSIS OF VARIANCE
Source DOF UTS Compression Strength Flexural Strength
SS MS FTest P SS MS FTest P SS MS FTest P
Layer
Thickness 2 8.102 4.051 0.52 0.656 6.3 3.1 0.02 0.976 1363.6 681.8 5.33 0.158
Shell
Thickness 2 102.3 51.16 6.62 0.131 504.9 252.4 2.01 0.333 623.3 311.7 2.44 0.291
Infill
Density 2 33.84 16.92 2.19 0.313 47.6 23.8 0.19 0.841 824.0 412.0 3.22 0.237
Error 2 15.44 7.72 252.4 126.2 255.8 127.9
Total 8 159.7 811.2 3066.7
DOF-degree of freedom; SS-sum of square; MS-mean sum of square, FTest 2,8 = 4.46
Individual ANOVA for UTS (Ultimate Tensile Strength), compression and flexural
strengths revealed that layer thickness for flexural strength and shell thickness for UTS were
significant. But, compressive strength was not influenced by any of the parameters studied.
This observation is in concurrence with elsewhere studies of ANOVA for maximum flexural
strength [16] which showed a dominant, statistically significant effect of layer thickness and
no significance for shell thickness and infill density.
Normal probability plots for UTS, compression and Flexural strength are shown in Fig. 3,4
and 5. The points lie approximately on the straight line and indicate that the underlying
distribution is normal. The probability plots shown in Fig 3,4 and 5 also show the analysis of
the residual error off the process parameter.
International Journal Of Advancement In Engineering Technology, Management and
Applied Science (IJAETMAS)
ISSN: 2349-3224 || www.ijaetmas.com || Volume 05 - Issue 01 || May-2017 || PP. 202-212
www.ijaetmas.com Page 206
Fig. 3 Normal probability plot of residual at 95% of confidence interval: UTS
Fig. 4 Normal probability plot of residual at 95% of confidence interval: Compression Strength
Fig. 5 Normal probability plot of residual at 95% of confidence interval: Flexural Strength
B. Multiple Regression model based on Experimental Responses
Multiple regression models were developed using MINITAB 16 version for the
experimental responses.
Analysis of the experimental data using full quadratic response is given as[14]:
𝑦𝑖 = 𝛽0 + 𝛽1𝑥1 + 𝛽𝑗𝑗 𝑥𝑗
𝑚
𝑗=1
𝑚
𝑗=1
𝑥𝑗 + 𝛽𝑗𝑘 𝑥𝑗𝑥𝑘
𝑗<𝑘
………… . (1)
Where yi is the response, xj is jth
factor, m is total number of factors. Final response surface
equations for UTS, compression and flexural strengths are given in the table 4 obtained from
equation 1. The coefficient of determination (R2) indicates the percentage of total variation in
3020100-10-20-30
99
95
90
80
70
60
50
40
30
20
10
5
1
ResidualP
erc
en
t
Normal Probability Plot(response is UTS, MPa)
3020100-10-20-30
99
95
90
80
70
60
50
40
30
20
10
5
1
Residual
Pe
rce
nt
Normal Probability Plot(response is Compression)
100500-50-100
99
95
90
80
70
60
50
40
30
20
10
5
1
Residual
Pe
rce
nt
Normal Probability Plot(response is Flexural Strength, MPa)
International Journal Of Advancement In Engineering Technology, Management and
Applied Science (IJAETMAS)
ISSN: 2349-3224 || www.ijaetmas.com || Volume 05 - Issue 01 || May-2017 || PP. 202-212
www.ijaetmas.com Page 207
the responses is 90.3%, 71.86% and 91.9% for UTS, compression and flexural strength
respectively.
TABLE 4
MULTIPLE REGRESSION MODELS FOR UTS, COMPRESSION AND FLEXURAL STRENGTHS
Response Regression Model Experimental
(MPa)
Predicted
(MPa)
UTS UTS = -8.579+158.427A -0.982 B+1.648C
R2 = 90.3% 37.925 35.067
Compression
Strength
Compression = 74.2934-62.819A-51.03 1.5+1 .165 C R2
=73.86 % 48.150 38.066
Flexural
Strength
Flexural = 97.876+178.48 A +7.025B -0.236C
R2 = 91.9% 152.74 149.41
A= Layer Thickness B= Shell Thickness and C= Infill Density
From, multiple regression models it can be concluded that average relative error between
the predicted value obtained by the model and experimental result shown in Table 4 are
found to be 2%, 9% and 4% for UTS, compression and flexural strengths. Small percentage
of errors proves the suitability of the models.
C. Responses Grey Relation Analyses
Grey relational analyses of experimental data are measured features of quality
characteristics which are first normalized ranging from zero to one. This process is known as
Grey relational generation. Based on normalized experimental data, Grey relational
coefficient is calculated to represent the correlation between the desired and the actual
experimental data. Overall Grey relational grade is determined by averaging the Grey
relational coefficient corresponding to selected responses. The overall performance
characteristic of the multiple response process depends on the calculated Grey relational
grade. This approach converts a multiple response process optimization problem into a single
response optimization with the objective function which is the overall Grey relational grade.
The optimal parametric combination is then evaluated which would result in the highest Grey
relational grade. The optimal factor setting for maximizing overall Grey relational grade can
be obtained by Taguchi method.
In Grey relational generation, the normalized Ra values corresponding to the larger-the-
better criterion which can be expressed as:
xi k = yi k −min yi k
max yi k −minyi k ……………….(2)
Where xi (k) is the value after the Grey relational generation, min yi(k) is the smallest value
of yi(k) for the kth
response, and max yi(k) is the largest value of yi(k) for the kth
response. An
ideal sequence is [x0(k) (k=1, 2, 3......, 9)] for the responses. The definition of Grey relational
grade in the course of Grey relational analysis is to reveal the degree of relation between the
9 sequences [x0(k) and xi(k), i=1, 2, 3,.......,9]. The Grey relational coefficient i(k) can be
calculated as:
𝑖 𝑘 = min −max
oi k + max…………… (3)
International Journal Of Advancement In Engineering Technology, Management and
Applied Science (IJAETMAS)
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Where Δ0i = xo(k)−xi(k) the absolute value of the difference of x0(k) and xi(k); is the
distinguishing coefficient 0≤≤1; Δmin =∀jmin
∈i∀kmin
xo(k)−xi(k) = the smallest value of
0i; and Δmax =∀jmax
∈i∀kmax
xo(k)−xi(k) is the largest value of 0i. After averaging the Grey
relational coefficients, the Grey relational grade i can be computed as:
i = 1/𝑛 𝑖(𝑘) ………… . (4)
𝑛
𝑘=1
Where n is the number of process responses. The higher value of Grey relational grade
corresponds to intense relational degree between the reference sequence x0(k) and the given
sequence xi(k). The reference sequence x0(k) represents the best process sequence.
Therefore, higher Grey relational grade means that the corresponding parameter
combination is closer to the optimal. The mean response for the Grey relational grade with its
grand mean and the main effect plot of Grey relational grade are very important because
optimal process condition can be evaluated from this plot [14].
TABLE 5
INFLUENCE OF PROCESS PARAMETERS OF GREY RELATIONAL GRADE
Expt. No. Grey Relational grade Order
UTS Compression Flexural Grade
1. 0.0390 0.0380 0.0536 0.0948 1
2. 0.0788 0.0395 0.1111 0.1553 6
3. 0.0675 0.1111 0.0919 0.2092 9
4. 0.0455 0.0464 0.0462 0.1073 3
5. 0.0950 0.0519 0.0504 0.1637 7
6. 0.0370 0.0487 0.0530 0.1033 2
7. 0.0617 0.0370 0.0486 0.1149 4
8. 0.1111 0.0535 0.0370 0.1769 8
9. 0.0420 0.0831 0.0710 0.1487 5
The grey relation coefficients for each performance characteristic was calculated using
equations (1) to (4) as shown in Table 5 and Table 6 shows the grey relational grade and
order using the experimental layout. As per the grey relational grade, tensile, compression
and flexural strengths are maximized at layer thickness of 0.1mm shell thickness of 1.5mm
and 40% of infill density.
TABLE 6
RESPONSE FOR GREY RELATIONAL GRADE
Process Parameter Level 1 Level 2 Level 3 Max-Min Order
Layer Thickness 0.3198 0.3054 0.3413 0.0359 3
Shell Thickness 0.2404 0.3779 0.3620 0.1375 2
Infill Density 0.2570 0.3121 0.4112 0.1542 1
Mean value of grey relational grade = 0.2248
The mean response refers to the mean of the performance characteristic for each parameter
at different levels. The difference between level 1 and 3 indicates that infill density has the
highest effect ( = max-min = 0.1542) followed by shell thickness ( = max-min = 0.1375)
and layer thickness ( = max-min = 0.0359).
International Journal Of Advancement In Engineering Technology, Management and
Applied Science (IJAETMAS)
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D. Optimisation of parameters using Response Surface Methodology (RSM)
Response surface methodology is a collection of statistical and mathematical techniques
useful for developing, improving and optimizing processes. The most extensive applications
of RSM are in the situations where several input variables potentially influence some
performance measure or quality characteristic of the process. This performance measure or
quality characteristic is called the response.
From the response surface plots, UTS as shown in Fig. 6 increases as shell thickness
increases and UTS decreases as layer thickness increases. UTS increases with layer thickness
and decreases with infill density as shown in Fig. 7. Initially UTS increases with shell
thickness as shown in Fig. 8, but it decreases for higher value of shell thickness.
Fig. 6 Response surface plots, UTS variation with increasing shell thickness
Fig. 7 Response surface plots, UTS variation with layer thickness and infill density
Fig. 8 Response surface plots, UTS variation with higher shell thickness
0
20
0.50.51.0
0.5
40
0.11.5
0.2
0.3
UT S, MPa
layer thickness
shell thickness
infill density 30
Hold Values
Surface Plot of UTS, MPa vs layer thickness, shell thickness
10
20
0.10.10.2
0.1
30
200.3
30
40
UT S, MPa
infill density
layer thickness
shell thickness 1
Hold Values
Surface Plot of UTS, MPa vs infill density, layer thickness
International Journal Of Advancement In Engineering Technology, Management and
Applied Science (IJAETMAS)
ISSN: 2349-3224 || www.ijaetmas.com || Volume 05 - Issue 01 || May-2017 || PP. 202-212
www.ijaetmas.com Page 210
From the response surface plots, it can be noted that compression strength as shown in Fig.
9, nominally increases as shell thickness increases. But compression strength decreases at
higher layer thickness. It also increases with decrease in layer thickness and increase in infill
density as shown in Fig. 10. Compression strength as shown in Fig. 11 increases with
increase in infill density and decrease in shell thickness.
Fig. 9 Response surface plots, compression variation with increasing shell thickness
Fig. 10 Response surface plots, compression variation with infill density
Fig. 11 Response surface plots, compression variation for infill density and shell thickness
0
15
30
0.10.10.2
0.1
30
45
0.50.3
1.0
1.5
Compression
Shell T hickness
Layer T hickness
Infill Density 30
Hold Values
Surface Plot of Compression vs Shell Thickness, Layer Thickness
0
10
20
0.10.10.2
0.1
20
30
200.3
30
40
Compression
Infill Density
Layer T hickness
Shell Thickness 1
Hold Values
Surface Plot of Compression vs Infill Density, Layer Thickness
0
15
30
0.51.0
0.51.0
30
45
201.5
30
20
40
Compression
Infill Density
Shell T hickness
Layer Thickness 0.2
Hold Values
Surface Plot of Compression vs Infill Density, Shell Thickness
International Journal Of Advancement In Engineering Technology, Management and
Applied Science (IJAETMAS)
ISSN: 2349-3224 || www.ijaetmas.com || Volume 05 - Issue 01 || May-2017 || PP. 202-212
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Response surfaces plot indicates that flexural strength increases at lower shell thickness as
shown in Fig. 12 and layer thickness. It increases with increase in layer thickness as shown in
Fig. 13 and infill density. Fig. 14 indicates that maximum flexural strength occurs at
minimum layer thickness and maximum infill density.
Fig. 12 Response surface plots, flexural variation with increasing shell thickness
Fig. 13 Response surface plots, flexural variation with layer thickness and infill density
Fig. 14 Response surface plots, flexural variation for minimum layer thickness and maximum infill density.
IV. CONCLUSIONS
Parametric study of Fused Deposition Modelling was performed by fabricating UTS,
compression and flexural specimens using ABS material by considering layer thickness, shell
thickness and infill density. Based on the experimental results the following conclusions were
arrived at:
Significant influence of shell thickness on UTS and layer thickness on flexural
strength was observed based on Analysis of Variance. However, none of the three
parameters was found to influence the compression strength.
0
50
100
0.50.51.0
0.5
100
150
0.11.5
0.2
0.3
Flexural
layer thickness
shell thickness
infill density 30
Hold Values
Surface Plot of Flexural vs layer thickness, shell thickness
30
60
90
0.51.0
0.51.0
120
201.5
30
20
40
Flexural
infill density
shell thickness
layer thickness 0.2
Hold Values
Surface Plot of Flexural vs infill density, shell thickness
International Journal Of Advancement In Engineering Technology, Management and
Applied Science (IJAETMAS)
ISSN: 2349-3224 || www.ijaetmas.com || Volume 05 - Issue 01 || May-2017 || PP. 202-212
www.ijaetmas.com Page 212
Multiple Regression models for UTS, flexural and compression strengths
predicted the responses with 2, 4 and 9 % errors respectively.
As per the grey relational grade, UTS, compression and flexural strengths are
maximized at layer thickness of 0.1 mm shell thickness of 1.5 mm and 40 % infill
density.
ACKNOWLEDGMENT
The authors express sincere gratitude to Mr. Shrikanth, 3D Protomaker IMEC technologies,
Nagarbhavi, Bangalore, for extending the facilities for conducting experiments.
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