International Journal Of Advancement In Engineering …€¦ · · 2017-06-04International...

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




www.ijaetmas.com 03

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.





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)





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.





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)





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)





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).





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





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





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





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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International Journal Of Advancement In Engineering …€¦ · · 2017-06-04International...

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