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1 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 Singh 6 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.1 mm, shell thickness of 1.5 mm and infill density of 4 %. 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 layerbased 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 [1] 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 Page 2

2 lengthwise. Breaking strain of the specimens with layers in the longitudinal direction were approximately % 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 % 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.2 mm in the machine gives decent prints. For high quality prints layer thickness of.1 mm may be used at the cost of building time which is twice that for.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.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-15 concentration range weight percentage of , specific gravity of 1.4 g / cm3 from Samsung Cheil Industries Ltd was used for fabricating the FDM specimens. Page 3

3 TABLE 1 SPECIFICATIONS OF 3D PROTOMAKER IMEC TECHNOLOGIES, BANGALORE Parameter Details Parameter Details Maximum build size 25 x 25 x mm3 Print Accuracy 5 micron Build material PLA and ABS Power requirements 12V DC, 15A Filament Diameter 1.75 mm and 3 mm CAD input data file format STL supported Layer thickness.15 to.3 mm 3D printing software Pronterface with Slic3r Printing modes Solid, honeycomb and hollow Operating system Windows XP, Windows 7 Printing temperature 17 to C for PLA to 24 C for ABS Power consumption 18 W Tensile, compression and flexural specimens as per ASTM D638, ASTM D79 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 L 9 Orthogonal Array lay-out. Fig. 1 FDM-3D Protomaker Fig. 2 UTM- Universal testing Machine (UTM) Page 4

4 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 Layer Thickness, mm Shell Thickness, mm Infill Density, % Measured Experimental Responses, MPa UTS Compression Strength Flexural Strength 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 Shell Thickness Infill Density Error Total 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. Page 5

5 Percent Percent Percent International Journal Of Advancement In Engineering Technology, Management and 99 Normal Probability Plot (response is UTS, MPa) Residual 1 Fig. 3 Normal probability plot of residual at 95% of confidence interval: UTS 99 Normal Probability Plot (response is Compression) Residual 1 Fig. 4 Normal probability plot of residual at 95% of confidence interval: Compression Strength 99 Normal Probability Plot (response is Flexural Strength, MPa) Residual 5 1 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]: m y i = β + β 1 x 1 + β jj x j x j + j=1 m j=1 j<k β jk x j x k. (1) Where y i is the response, x j is j th 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 (R 2 ) indicates the percentage of total variation in Page 6

6 the responses is 9.3%, 71.86% and 91.9% for UTS, compression and flexural strength respectively. TABLE 4 MULTIPLE REGRESSION MODELS FOR UTS, COMPRESSION AND FLEXURAL STRENGTHS UTS Response Compression Strength Flexural Strength Regression Model Experimental (MPa) Predicted (MPa) UTS = A B C R 2 = 9.3% Compression = A C R 2 =73.86 % Flexural = A B C R 2 = 91.9% 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-thebetter criterion which can be expressed as: xi k = yi k min yi k.(2) max yi k minyi k Where x i (k) is the value after the Grey relational generation, min yi(k) is the smallest value of yi(k) for the k th response, and max yi(k) is the largest value of yi(k) for the k th response. An ideal sequence is [x (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 [x (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) Page 7

7 Where Δ i = x o (k) x i (k) the absolute value of the difference of x (k) and x i (k); is the distinguishing coefficient 1; Δmin = j min i k min x o (k) x i (k) = the smallest value of i ; and Δ max = j max i k max x o (k) x i (k) is the largest value of i. After averaging the Grey relational coefficients, the Grey relational grade i can be computed as: n i = 1/n i(k). (4) 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 x (k) and the given sequence x i (k). The reference sequence x (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 Grey Relational grade Expt. No. UTS Compression Flexural Grade Order 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.1mm shell thickness of 1.5mm and 4% of infill density. TABLE 6 RESPONSE FOR GREY RELATIONAL GRADE Process Parameter Level 1 Level 2 Level 3 Max-Min Order Layer Thickness Shell Thickness Infill Density Mean value of grey relational grade =.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 =.1542) followed by shell thickness ( = max-min =.1375) and layer thickness ( = max-min =.359). Page 8

8 International Journal Of Advancement In Engineering Technology, Management and 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. Surface Plot of UTS, MPa vs layer thickness, shell thickness infill density 4 UT S, M P a shell thickness 1.5 layer thickness.1 Fig. 6 Response surface plots, UTS variation with increasing shell thickness Surface Plot of UTS, MPa vs infill density, layer thickness shell thickness 1 UT S, MP a layer thickness.3 infill density Fig. 7 Response surface plots, UTS variation with layer thickness and infill density Fig. 8 Response surface plots, UTS variation with higher shell thickness Page 9

9 International Journal Of Advancement In Engineering Technology, Management and 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. 1. Compression strength as shown in Fig. 11 increases with increase in infill density and decrease in shell thickness. Surface Plot of Compression vs Shell Thickness, Layer Thickness Infill Density 45 C ompr ession Layer T hickness Shell T hickness.5.3 Fig. 9 Response surface plots, compression variation with increasing shell thickness Surface Plot of Compression vs Infill Density, Layer Thickness Shell Thickness 1 Compression Layer T hickness.3 Infill Density Fig. 1 Response surface plots, compression variation with infill density Surface Plot of Compression vs Infill Density, Shell Thickness Lay er Thick ness.2 45 C ompr ession Shell T hickness 1.5 Infill Density Fig. 11 Response surface plots, compression variation for infill density and shell thickness Page 21

10 International Journal Of Advancement In Engineering Technology, Management and 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. Surface Plot of Flexural vs layer thickness, shell thickness infill density 15 1 Flexur al shell thickness layer thickness Fig. 12 Response surface plots, flexural variation with increasing shell thickness Surface Plot of Flexural vs infill density, shell thickness layer thickness.2 1 Flexur al shell thickness 1.5 infill density 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. Page 211

11 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.1 mm shell thickness of 1.5 mm and 4 % infill density. ACKNOWLEDGMENT The authors express sincere gratitude to Mr. Shrikanth, 3D Protomaker IMEC technologies, Nagarbhavi, Bangalore, for extending the facilities for conducting experiments. REFERENCES [1] Dario Croccolo, Massimiliano De Agostinis and Giorgio Olmi, Experimental characterization and analytical modelling of the mechanical behaviour of fused deposition processed parts made of ABS-M, Compu Mater Sci 79 (13), pp [2] Hopkinson N, Hagur R J M and Dickens P H, Rapid manufacturing: an industrial revolution for the digital age. England: John Wiley & Sons Ltd, 6 [3] Bernarand and Fischer, New trends in rapid product development, CIRP annals-manuf Tech, (2), 51(2), pp [4] Yonghua Chen and Jianan Lu, Minimise joint clearance in rapid fabrication of non-assembly mechanisms, Int J Computer Inte Manuf, Vol. 24, No. 8, August (11), pp [5] Alberto Boschetto and Luana Bottini, Roughness prediction in coupled operations of fused deposition modeling and barrel finishing, J Mater Proce Tech, (15), pp [6] Peng An Hua and Xiao XingMing, Investigation on Reasons Inducing Error and Measures Improving Accuracy in Fused Deposition Modeling, Adv inform Sci and Service Sci (AISS), (12),4(5), pp [7] Galantuccia L M, Bodib I, Kacani J and Lavecchia F, Analysis of dimensional performance for a 3D open-source printer based on fused deposition modeling technique, 3rd CIRP Global Web Conference, Procedia CIRP 28, (15), pp [8] Nur Saaidah Abu bakar, Mohd rizal alkahari and Hambali boejang, Analysis on fused deposition modelling performance, J Zhejiang University-Sci A (Applied Physics & Engineering) (1), 11(12), pp [9] Nur Saaidah Abu Bakar, Mohd Rizal Alkahari and Hambali Boejang, Analysis on Fused Deposition Modeling (FDM) Performance The 11th Asia, Pacific Ind Engg and Manag Syst Conference Melaka, 7 1 Dec (1), pp.1-6 [1] Jose Stockler Canabrava Filho, Rodrigo de Souza Dantas, Victor Jayme Roget Rodrigues Pita and Francisco Jose de Castro Moura Duarte, Influence of the direction of parts modelling by polymer Deposition on their strength, 22nd International Congress of Mechanical Engineering (COBEM 13), Nov 3-7, (13), pp [11] Anoop Kumar Sood, Ohdar R K and Mahapatra S S, Parametric appraisal of mechanical property of fused deposition modelling processed parts, Mater and Design 31 (1), pp [12] Dinesh Kumar S, Nirmal Kannan V and Sankaranarayanan G, Parameter Optimization of ABS-Mi Parts Produced by Fused Deposition Modeling for Minimum Surface Roughness, Int J Current Engg and Tech, (14), pp [13] Anoop Kumar Sood, Ohdar R K and Mahapatra S S, Improving dimensional accuracy of Fused Deposition Modelling processed part using grey Taguchi method, Mater and Design (9), pp [14] Anoop Kumar Sood and Ohdar R K, Grey Taguchi Method for Improving Dimensional Accuracy of FDM Process, AIMS International Conference on Value-based Management Aug 11-13, (1), pp [15] Dietmar Drummer, Sandra Cifuentes Cuellar and Dominik Rietzel, Suitability of PLA/TCP for fused deposition Modelling, Rapid Prototyping J, (12), pp.5 57 [16] Cany Mendonsa, Naveen K V, Prathik Upadhyaya and Vyas Darshan Sheno, Influence of FDM Process Parameters on Build Time Using Taguchi and ANOVA Approach, Int J Sci and Research, (13), pp Page 212