INVESTIGATION ON THE EFFECT OF TENSILE STRENGTH ON FDM BUILD PARTS USING TAGUCHI-GREY RELATIONAL BASED MULTI-RESPONSE OPTIMIZATION

Transcription

1 International Journal of Mechanical Engineering and Technology (IJMET) Volume 8, Issue 12, December 2017, pp , Article ID: IJMET_08_12_006 Available online at ISSN Print: and ISSN Online: IAEME Publication Scopus Indexed INVESTIGATION ON THE EFFECT OF TENSILE STRENGTH ON FDM BUILD PARTS USING TAGUCHI-GREY RELATIONAL BASED MULTI-RESPONSE OPTIMIZATION J. Santhakumar, U.Mohammed Iqbal and M.Prakash Department of Mechanical Engineering, SRM Institute of science and technology, Kattankulathur, Tamil Nadu, India ABSTRACT Fused deposition modelling (FDM) is one of the rapid prototyping technologies using additive fabrication approach. The contribution of three parameters namely layer thickness, build orientation and fill density are the most significant process parameter to the strength of FDM product. In this experiment, an attempt has been made to identify the process parameters that influence the strength of parts made with Acrylonitrile butadiene styrene (ABSM30). The L 9 orthogonal Array is used to obtain the experimentation runs, and by the grey relational analysis, a set of optimal process parameter combination was identified by considering the comprehensive mechanical properties such as elongation at yield, elongation at break and tensile strength of FDM parts. Results show that these parameters have a significant effect on the finished products at various levels. It is also found that the tensile strength of processed parts is greatly influenced by layer thickness parameter followed by build orientation and fill density. The obtained results can be used for the characteristics of fabricated parts to be further refined. Keywords: Additive Manufacturing, Fused deposition modelling, Fill density, Tensile strength and Taguchi Grey relation. Cite this Article: J. Santhakumar, U.Mohammed Iqbal and M.Prakash, Investigation on the Effect of Tensile Strength on Fdm Build Parts Using Taguchi-Grey Relational Based Multi-Response Optimization, International Journal of Mechanical Engineering and Technology 8(12), 2017, pp editor@iaeme.com

2 Investigation on the Effect of Tensile Strength on Fdm Build Parts Using Taguchi-Grey Relational Based Multi-Response Optimization 1. INTRODUCTION Rapid Prototyping (RP) also known as Layered Manufacturing is a group of technologies that allow for producing a physical prototype from the 3D CAD model, without the need for any tooling. RP technologies have found their place among other, traditional manufacturing techniques, due to their quick manufacturing process of a physical prototype from the designed part [1]. Fused deposition modelling is one among the RP technologies. FDM works on an "additive" principle by laying down material in the layer, which builds the products by adding/depositing raw material, layer by layer, re- placing a conventional metal removal process [2]. Although there are many advantages concerning reduction in product build time for complex shape parts and production of components without the use of tools by these processes, they have their own relative merits and demerits. Hence, it is necessary to understand the shortcomings of a process before recommending for industrial application. It has been proposed that improvement of surface quality, part strength, build time, accuracy and repeatability are critical issues to be addressed for successful implementation of RP technology [3]. Many researchers have made attempts to improve the part accuracy, surface finish, strength, etc. by proper selection of process parameters. Pandey and Ragunath [6] studied the shrinkage phenomena in SLS part along with process parameters such as laser power, scan length, and beam speed, and they observed that the laser power and scan length are most influencing process variables along X direction, laser power, and beam speed are significant along Y direction, and beam speed, hatch spacing, and part build temperature are significant along Z direction. Vasudevarao et al.[7] indicated that layer thickness and part orientation have a significant effect on the roughness of parts fabricated using FDM 1650 machine. Es Said et al. [8] have shown that anisotropic behaviour on mechanical properties is caused mainly due to raster orientation when ABSP400 samples are built on FDM 1650 machine. Khan et al. [9] identified significant parameters and their levels for improving the flexibility of FDM part using the design of experiments approach. Anitha et al. [11] use taguchi method to determine the effect of layer thickness, road width and deposition speed each at three levels of the surface roughness of component produced using FDM process. The results indicate that layer thickness is the most influencing process parameter affecting surface roughness followed by road width and deposition speed. Zhou et al. [12] studied the effect of five control factors such as layer thickness, over cure, hatch spacing, blade gap, and part location on build platform and few selected interactions on the accuracy of SLS parts. It has been observed that the factor settings for maximum efficiency depend on geometrical features in part. Campanelli et al. [13] have recommended that hatch over cure and border over cure must be set at their maximum level for improving part accuracy when the layer thickness is high. From the literature review, it is observed that the properties of RP parts are dependent on various process related parameters and also suggested that studies on the effect of process parameters on improving the quality of FDM built parts, specifically, strength accuracy, have been devoted to a limited extent. Grey taguchi method is used in this work to generate a single response from different performance characteristics. 2. EXPERIMENTAL DETAILS In this work Acrylonitrile butadiene styrene (ABS M30) material is used for fabricating the specimens. Fortus FDM-400MC machine was used to fabricate the tensile test specimen then the samples was cut into required shape as per ASTM D638 standard as shown in Figure 1. A tensile test was carried out on INSTRON 1195 Universal testing machine editor@iaeme.com

3 J. Santhakumar, U.Mohammed Iqbal and M.Prakash Figure 1 ASTM D638 standard (Dimensions in mm ) 3. DESIGN OF EXPERIMENTS The level of each process parameter is shown in Table 1. For the design of experiments, Taguchi method is one of the uncomplicated and efficient methods [12]. The L 9 Orthogonal Array used in this study. Signal to noise (S/N) ratio is used to determine the influence and variation caused by each factor and interaction about the total variation observed in the result. The quality characteristic employed in this study was 'the-bigger-the-better' for tensile strength and elongation at break while smaller-the-better' is used for elongation at yield. Moreover, the results behave linearly when expressed in terms of S/N ratios. Table 1 Levels of Process Parameters Parameter Level 1 Level 2 Level 3 Layer Thickness(mm) Build Orientation(degrees) Fill Density (%) Tensile Test Results Tests were performed according to the American standard ASTM D638, at an ambient temperature of 23 C and a relative humidity of 50%. The velocity was 5 mm/min and the specimens were loaded until they broke. A load cell with 5kN was used for this test and result obtained as listed in Table 2. Figure 2 shows the tensile fractured specimen. Specimen Number Layer thickness (mm) Build Orientation (deg) Table 2 Tensile test Results Fill Density (%) Tensile Strength (Mpa) Elongation at Yield(%) Elongatio n at Break (%) editor@iaeme.com

4 Investigation on the Effect of Tensile Strength on Fdm Build Parts Using Taguchi-Grey Relational Based Multi-Response Optimization Figure 2 Tensile Test breaking specimen 3.2. Multi-Objective Optimisation Grey Relational Analysis (GRA) is a recommended method for optimizing the complicated inter-relationships among multiple response characteristics [14]. Since finding the highest Ultimate Tensile Strength is the objective we apply the Larger-the-better criteria. In thermoplastics, the Elongation at Yield should be lower as they should not elongate like elastomer and hence the Smaller-the-better criteria. However, the Elongation at Break should be high so that there isn't a sudden fracture. Therefore, we use the Larger-the-better criteria. The S/N ratios of the experimental results based on their respective criterion are shown in Table 3. Specimen Number Layer Thickness (mm) Build Orientati on (deg) Table 3 Signal to Noise Ratios Fill Density (%) Tensile Strength (db) S/N Ratios Elongation at Yield (db) Elongation at Break (db) STEP 1: Calculation of Grey Relational Coefficient. The grey relational coefficient is calculated to express the relationship between the ideal and actual normalized experimental results. Before that, the deviation sequence for the reference and comparability sequence were found out. The grey relational coefficient is given in Table 4. Following data pre-processing, a grey relational coefficient can be calculated with the preprocessed sequences [15] editor@iaeme.com

5 J. Santhakumar, U.Mohammed Iqbal and M.Prakash Specimen Number Layer Thickness (mm) Table 4 Grey Relational Coefficient Build Orientati on (deg) Fill Density (%) Tensile Strength(dB) Grey Relational Coefficient Elongation at Yield(dB) Elongation at Break(dB) The multiple performance characteristics can be optimized by producing single grey relational grade from various coefficients of performance characteristics. A higher grey relational grade corresponds to the best optimal setting of process parameters for multiresponses as shown in Table 5. The Taguchi method is a systematic application of planning and analysis of experiments to improve product quality. In re- cent years, the Taguchi method has become a powerful tool for improving productivity during research and development also the Grey relational grade, which is obtained by integrating the Grey relational coefficients corresponding to each experiment[15]. Specimen Number Tensile Strength (Mpa) Table 5 Grey Relational Grade with Rank Grey Relational Coefficients Elongation at Yield (%) Elongation at Break (%) Grade Rank The larger the grey relational grade, the better is the multiple performance characteristics. However, the relative importance among the machining parameters for the multiple performance characteristics still needs to be known, so that the optimal combinations of the machining parameter levels can be determined more accurately. With the help of Table 6, the optimal parameter combination was identified as A1 (Layer Thickness, ), B1 Build orientation, ) and C1 (Fill Density, ) editor@iaeme.com

6 Investigation on the Effect of Tensile Strength on Fdm Build Parts Using Taguchi-Grey Relational Based Multi-Response Optimization Table 6 Average for GRA Each Input Parameters Control factor Level 1 Level 2 Level 3 Max-Min Rank Layer Thickness Build Orientation Fill Density Total mean grey relational grade = RESULTS AND DISCUSSIONS From the Taguchi-Grey analysis, it is apparent that the Layer thickness is the most significant and influential parameter among the given three parameter. The main effects plots confirm this for Tensile Strength, Elongation at Yield and Elongation at Break. Fig. 3a shows the details of the main effects plot for S/N Ratios corresponding to the response values of Tensile Strength. The chosen characteristic is Larger is better.' As can be observed, the largest means correspond to the Level 1 values for Layer Thickness, Build Orientation, and Fill Density respectively. The steepness of the curve concerning the horizontal axis shows that the parameter contributes significantly to the response factor, at the corresponding level. Figure 3 (a-c) Main Effects Plot for S/N Ratios Figure 3b. Shows the details of the main effects plot for S/N Ratios corresponding to the response values of Elongation at Yield. The chosen characteristic is Smaller is better'. It is observed that, the smallest means correspond to the Level 1 values for Layer Thickness, Build Orientation, and Fill Density respectively. The steepness of the curve concerning the horizontal axis shows that the parameter contributes significantly to the response factor, at the corresponding level. Hence, it can be determined that Level 1 values are the desired optimal values to enhance Elongation at Yield. Figure 3c. Shows the details of the main effects plot for S/N Ratios corresponding to the response values of Elongation at Break. The chosen characteristic is Larger is better'. It is to understand that, the largest means correspond to the Level 1 values for Layer Thickness, Build Orientation, and Fill Density respectively. The steepness of the curve concerning the horizontal axis shows that the parameter contributes significantly to the response factor, at the corresponding level. Hence, it can be determined that Level 1 values are the desired optimal values to enhance Elongation at Break. As the objective of this study is to obtain optimum process parameters for obtaining maximum Tensile Strength, least Elongation at Yield and maximum Elongation at Break, it can be inferred that for a layer thickness of mm, Build Orientation of 0 degrees and Fill Density of 50%, we obtain the desired results. After identifying the optimal process parameters, the confirmation test is to be conducted to validate the analysis. In the conformation test, an experiment has been carried out with optimal process parameters 58 editor@iaeme.com

7 J. Santhakumar, U.Mohammed Iqbal and M.Prakash settings. It is observed that there is 2.5% improvement in the gray relation grade value of conformation experiments from the predicated mean value Microstructure of Fractured Specimens Figure 4 illustrates the closer observation of the fractured specimen using Scanning Electron Microscope (SEM). It reveals that the presence of air gaps and voids which are responsible for weaker bonding between layers and hence lower tensile strength values. The cross-section of the fracture specimens shows parallel layers that are generated by FDM process. It is also observed from Figure 6 that the failure is caused due to the pulling and rupturing of rasters and the material separation occurs in a plane approximately normal to the tensile stress. Figure 4 SEM image of specimen failed due to tensile load 5. CONCLUSION In this study, Taguchi L 9 array with grey relational analysis has been used to optimize the multiple performance characteristics such as tensile strength, elongation at break and elongation at yield. The following conclusions have been arrived from the study. There is an improvement of 2.5% in grey relation grade value with respect to predicted value. It is found that the optimal parameter combination is layer thickness ( ), build orientation ( ) and Fill Density ( ). Confirmation test proved that the determined optimum combination had satisfied the real requirement of input process parameters in FDM process as well as the effects of output quality characteristics have been observed below. The most significant observation is that a minimum fill density can afford to give a highly optimized value. This has huge implications for the amount and cost of the material. The weak interlayer bonding is responsible for the decrease in strength because distortion occurs due to high-temperature gradient towards the bottom layers. As the layer thickness increases, less number of layers will be required, and distortion effect is minimized, and hence, strength increases. REFERENCES [1] P.M.Pandey, K.Thrimurthulu and N.Venkata Reddy, Optimal part deposition orientation in FDM by using a multi-criteria genetic algorithm, International Journal of Production Research, vol. 42, pp , [2] D.T. Pham and R.S. Gault, A Comparison of Rapid Prototyping Technologies, International Journal of Machine Tools and Manufacture, vol. 38, pp , [3] R.V. Rao and K.K. Padmanabhan, Rapid Prototyping Process Selection Using Graph Theory and Matrix Approach, Journal of Materials Processing Technology, vol. 194, pp. 81-8, editor@iaeme.com

8 Investigation on the Effect of Tensile Strength on Fdm Build Parts Using Taguchi-Grey Relational Based Multi-Response Optimization [4] X. Chen, C. Wang, X. Ye, Y. Xiao, and S. Huang, Direct Slicing from Powershape Models for Rapid Prototyping, International Journal of Advanced Manufacturing Technology, vol. 17, pp , 2001 [5] K. Thrimurthulu, P. M. Pandey and N. Venkata Reddy Optimum Part Deposition Orientation in Fused Deposition Modeling, International Journal of Machine Tools and Manufacture, vol. 44, pp , May [6] W. Han, M. A. Jafari, S. C. Danforth and A. Safari, Tool Path-Based Deposition Planning in Fused Deposition Processes, Journal of Manufacturing Science and Engineering, vol. 124, pp , [7] R.J. Wang, L. Wang, L. Zhao and Z. Liu, Influence of Process Parameters on Part Shrinkage in SLS, International Journal of Advance Manufacturing Technology, vol. 33, , [8] P. Pandey and N. Raghunath, Improving Accuracy through Shrinkage Modelling by using Taguchi Method in Selective Laser Sintering, International Journal of Machine Tools and Manufacture, vol. 47, pp , May [9] R. Anitha, S Arunachalam and P Radhakrishnan, Critical Parameters Influencing the Quality of Prototypes in Fused Deposition Modelling, Journal of Materials Processing Technology, vol. 118, pp , [10] G. Sui and M.C. Leu, Investigation of Layer Thickness and Surface Roughness in Rapid Freeze Prototyping, Journal of Manufacturing Science and Engineering, vol. 125, p. 556, [11] P.M. Pandey, N.V. Reddy and S.G. Dhande, Improvement of Surface Finish by Staircase Machining in Fused Deposition Modeling, Journal of Materials Processing Technology, vol. 132, pp , Jan [12] L.M. Galantucci, F. Lavecchia, and G. Percoco, Experimental Study Aiming to Enhance the Surface Finish of Fused Deposition Modeled Parts, CIRP Annals-Manufacturing Technology, vol. 58, pp , [13] K. Chockalingam, N. Jawahar, K.N. Ramanathan and P.S. Banerjee, Optimization of stereolithography process parameters for part strength using design of experiments, International Journal of Advanced Manufacturing Technology, vol.29, pp ,2006. [14] K. Chockalingam, N. Jawahar, and U. Chandrasekhar, Influence of layer thickness on mechanical properties in stereolithography, Rapid Prototyping Journal, vol. 12, pp , [15] Raghuraman S, Thiruppathi K, Panneerselvam T and Santosh S, Optimization of EDM Parameters Using Taguchi Method And Grey Relational Analysis For Mild Steel Is 2026, International Journal of Innovative Research in Science, Engineering and Technology, vol.2, pp ,july [16] Lin CL, Use of the Taguchi method and grey relational analysis to optimize turning operations with multiple performance characteristics, Materials and Manufacturing Processes, vol.19, pp , [17] Pham D.T and Dimov S.S, Rapid Manufacturing, Berlin, Springer, 2001, pp [18] T. Narendiranath Babu, Anil Mandliya, Akash Kumar Singh, Avneesh Singh and D. Rama Prabha, Evaluation of Tensile Properties of Epoxy Resin Based Composites Reinforced with Jute/Banana/Flax and Uni-Axial Glass Fibers.International Journal of Civil Engineering and Technology, 8(2), 2017, pp [19] Hassan Karim Mohammed, Dr. Mohammad Tariq, Dr. Fouad Alwan Saleh, Analysis of Turning Parameters During Turning of Aisi 1040 Steel Using Taguchi Method, International Journal of Advanced Research in Engineering and Technology (IJARET), Volume 5, Issue 3, March (2014), pp [20] V.Diwakar Reddy, A. Gopichand, G. Nirupama, G. Krishnaiah, Design and Fabrication of Corrugated Sandwich Panel Using Taguchi Method, International Journal of Design and Manufacturing Technology (IJDMT), Volume 4, Issue 2, May - August (2013), pp editor@iaeme.com