Process parameters optimization for friction stir welding of Polypropylene material using Taguchi s approach

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1 Journal of Scientific & Industrial Research Vol. 73, June 2014, pp Process parameters optimization for friction stir welding of Polypropylene material using Taguchi s approach K Lenin* 1, H Abdul Shabeer 2, K Suresh kumar 3 and K Panneerselvam 4 *1,3 Dept of Mechanical Engineering, Jayaram College of Engineering and Technology, Trichy, Tamil Nadu, India 2 Department of Electronic and Communication, AVS Engineering College, Salem, Tamil Nadu, India 4 Department of Production Engineering, National Institute of Technology, Tiruchirappalli, Tamil Nadu, India This paper deals with the successful application of friction stir welding (FSW) in joining a polypropylene material. Welding parameters such as tool pin profile, feed rate and tool rotational speed were selected with the objective of producing a good weld joint with maximum strength and minimum defects. The maximum strength was considered for the selection of process parameters. Taguchi method was used to analyze the effect of each welding process parameters and then to determine the optimal process parameters. From this experiment, it was found that the effects of welding parameters on the maximum strength were in the order of tool pin profile, feed rate, and tool rotational speed. The optimal combination of FSW parameters was the threaded pin profile, feed rate of 60 mm/min and tool rotational speed of 1500 rpm. A confirmation test was carried out and results were closer to the optimized results. Keywords: Polypropylene, friction stir welding, Taguchi approach, analysis of variance Introduction Friction stir welding has become a technology of widespread interest because of its numerous advantages, most important of which is its ability to weld otherwise unweldable alloys 1. It was invented and experimentally proven at The Welding Institute (TWI) UK in December FSW is an emerging solid state joining process 3 will not encounter problems like porosity, alloy segregation and hot cracking, and welds are produced with good surface finish 4. The effects of some important parameters such as rotational speed, feed rate, and tool pin profile on weld properties are the major topics for researchers 5, 6 & 24. Polypropylene (PP), also known as Polypropene (Thermoplastic Polymer), is a polymer of the polypropene monomer. It is semi-crystalline polyamides. Polypropylene is reasonably economical and widely used in automotive, aerospace, marine, industrial packing and labeling, textiles, ropes 7, 8 & 23. The Taguchi method is a systematic application of design and analysis of experiments for the purpose of designing and improving product quality 9 and at low cost 10. Taguchi s method is employed to relate the laser welding input parameters (beam power, travel speed and focal position) to the three responses i.e., tensile strength, bead width, and depth *Author for correspondence leninnaga@yahoo.com of penetration 11. This method was successfully applied to optimize the Nd: YAG laser welding onto magnesium alloy 12. Though research works applying Taguchi methods on fusion welding processes and casting method have been reported in literatures 13-15, it appears that the optimization of FSW process parameters of Polypropylene material using Taguchi method has not been analyzed yet. Based on the above facts, Taguchi L 16 orthogonal array method was adopted to analyze the effect of each processing parameters (Tool pin profile, Feed rate, and Tool rotational speed) for optimization of friction stir welded joints of polypropylene material. Taguchi method Taguchi method is a powerful problem solving tool which can improve the performance of the process, product, design and system with a minimum time and cost 16. The number of experiments increases with the increase of process parameters. To solve this complexity, Taguchi method uses a special design of orthogonal array to study the entire process parameter space with only a small number of experiments 17. Furthermore, Taguchi parameter design can reduce the fluctuation of system performance and quality of the source of variation 18. The basic steps for this method are summarized below 19 :

2 370 J SCI IND RES VOL 73 JUNE ) Identification of the quality characteristics and selection of process parameters to be evaluated. 2) Selection of the appropriate orthogonal array and assigning these parameters to the orthogonal array. 3) Conducting the experiments based on the arrangement of the orthogonal array. 4) Analyzing the experimental results using the signal to noise (S/N) ratio and analysis of variance (ANOVA). 5) Making decisions regarding optimum setting of the control parameters and predicting the results of each of the parameters at their new optimum levels. FSW process FSW was performed on a CNC vertical machining centre (FANUC) made in Hartford, Taiwan with a specially designed fixture. The polypropylene plate of 220 x 100 x 10 mm plate with tensile strength 25.5 MPa, Rockwell hardness 76, Shore D hardness 65, Izod strength 190 kn/m, and melting temperature 130 to 175 degree centigrade. The non-consumable rotating tools used in this study had four different tool pin profiles namely Square (SQ), taper (TC), triangular (TR) and threaded (TH) pin with a cylindrical shank. The fixed pin type tool made of mild steel with a nominal pin diameter of 6 mm and shoulder diameter of 24 mm and also pin length of 9.8 mm was used in the present investigation. The quality of the welds is dependent on the selection of the welding process parameters. In the present study, FSW experiments were carried out by varying the feed rate in the range of mm/min, the tool rotational speed in the range of rpm and pin profile of taper, triangular, square and threaded. To evaluate the quality of FSW of polypropylene, the different responses such as tensile strength, hardness (Shore-D, Rockwell), impact strength (Izod, Charpy) and nugget area were considered. These responses were measured based on the ASTM standards. The ASTM standards were utilized for different testing namely D (Standard test method for tensile properties of Plastics), D (Standard test methods for determining the Charpy impact resistance of plastics), D (Standard test methods for determining the Izod impact resistance of plastics), D (2010) (Standard test methods for determining the shore - D (Durometer) hardness testing of plastics), and D (Standard test method for Rockwell hardness of plastics). Scanning electron microscopy was used to take the SEM images. Characterizations of the different welded polypropylene plate were analyzed on the basis of the SEM images. Experimental design In this section, the use of Taguchi method to determine the process parameters in FSW of polypropylene is reported step-by-step. Selection of process parameters In the present investigation, four level process parameters, i.e., Tool pin profile, Feed rate and Tool rotational speed were considered. The values of the welding process parameter at the different levels are presented in Table 1. Selection of Orthogonal Array The experimental results for the friction stir welding process using L 16 orthogonal array are shown in Table 2. Calculation of S/N ratio In the Taguchi s method, the signal-to-noise ratio is used to measure the quality characteristics and also to evaluate the influence of each selected factor on the responses. Usually, there are three categories of performance characteristics for the analysis of the S/N ratio, i.e., the smaller the better, the larger the better, and the nominal the best. To obtain optimal parameters, the maximum responses are desired. Therefore, the larger the better characteristics form was selected for this study. The S/N ratio of the larger the better performance characteristics can be expressed as: 1 2 j 10log Yijk (1) n Where n is the number of tests and Y ijk is the experimental value of the i th quality characteristics in the j th experiment at the k th test. Level Table 1 Process parameters with their range and values at four levels Tool pin profile (A) Factors Feed rate (B) mm/min Tool rotational speed (C) rpm Range Level 1 Taper Level 2 Triangular Level 3 Square Level 4 Threaded

3 LENIN et al.: Process parameters optimization for friction stir welding of Polypropylene 371 Table 2 Experimental results for polypropylene materials in the percentage (%) Experimental level Tested values of welded samples in percentages (%) Tensile strength Shore-D hardness Rockwell hardness Izod strength Charpy strength Nugget area 1(A1,B1,C2) (A1,B2,C1) (A1,B3,C4) (A1,B4,C3) (A2,B1,C4) (A2,B2,C3) (A2,B3,C2) (A2,B4,C1) (A3,B1,C1) (A3,B2,C2) (A3,B3,C3) (A3,B4,C4) (A4,B1,C3) (A4,B2,C4) (A4,B3,C1) (A4,B4,C2) Table 3 ANOVA for average of S/N ratio Parameters DOF Sum of Square Mean Square F cal F Tab Contribution (%) Tool pin profile (A) Feed rate (B) Rotation Speed (C) Error Total Analysis of variance (ANOVA) ANOVA is a statistical decision making tool used for detecting any differences in average performances of tested parameters. The purpose of the ANOVA test is to investigate the significance of the process parameters which affects the different responses of FSW joints. It employs sum of squares and F statistics to find out relative importance of the analyzed processing parameters, measurement errors and uncontrolled parameters. ANOVA is used to check the adequacy of the model for the responses in the experimentation and it is used to calculate the F- ratio, which is the ratio between the regression mean square and the mean square error. If the calculated value of F-ratio is higher than the tabulated value of F-ratio, then the model is adequate at desired significance level to represent the relationship between response and the parameters. Results and discussion Table 3 shows the S/N ratio of each level for the tool pin profile, feed rate and tool rotational speed. The percentage of individual responses such as tensile strength, shore-d hardness, Rockwell hardness, Izod strength, Charpy strength and nugget area were converted into S/N ratio by using equation 1. S/N ratio analysis and characterization of experimental runs Taguchi recommends analyzing the S/N ratio using the conceptual approach that involves graphing the effects and visually identifying the factors that appear to be significant 20. In this investigation, the different responses were analyzed to determine the effect of FSW process parameters. The experimental results were then converted into signal-to-noise (S/N) ratio. The S/N ratio was calculated for every response and then average S/N ratio is graphically presented in Fig. 1. The maximum value occurred in the fourteenth experimental run and the minimum value occurred in the second experimental run. It is clear that a larger Fig. 1 Average of S/N Ratio of all responses with respect to Experimental runs

4 372 J SCI IND RES VOL 73 JUNE 2014 Fig. 2 (A) - SEM images for Experimental run 14 (Threaded pin, 40 mm/min, and 2250 rpm), (B) - SEM images for Experimental run 2 (Taper pin, 40 mm/min, and 1500 rpm) S/N ratio corresponds to better quality characteristics. Based on the average of S/N ratio, the optimum value was obtained in the fourteenth experimental run (threaded tool pin profile, feed rate of 40 mm/min and tool rotational speed of 2250 rpm). The average of the S/N ratio of all the responses was analyzed based on the maximum values. The highest values appeared in the fourteenth experimental run (threaded pin profile, feed rate of 40 mm/min, tool rotational speed of 2250 rpm) and moved on to the next step in the fifteenth (threaded pin profile, feed rate of 50 mm/min, tool rotational speed of 1500 rpm) experimental run and gradually got reduced in the sixteenth experimental run (threaded pin profile, feed rate of 60 mm/min, tool rotational speed of 1750 rpm) and then further decreased step by step in random order of experimental runs. Before that final condition, it reached low value as worst condition in first experimental run (taper pin profile, feed rate of 30 mm/min, tool rotational speed of 1750 rpm) and finally it reached very low value as very worst condition in second experimental run (taper pin profile, feed rate of 40 mm/min, tool rotational speed of 1500 rpm). Based on the all responses, the maximum strength had developed in the fourteenth experimental run. The SEM images of this experimental run are explained in Fig. 2.A. The overall defects occurred in this run was very minimum compared with the other experimental runs. First two tests were conducted by low magnification scales which had only small cracks formed as segments and cavities. Those cracks were enlarged in the remaining four SEM images. Apart from these crack and cavity, all remaining area had good surface morphology. Fig. 3 Response graph for average of S/N ratio In the second experimental run, the very worst case of welded region had occurred. The SEM images clearly explained these conditions as shown in Fig. 2.B. Lot of cavities, porous, burrs and blow holes appeared in this condition for all magnification levels. The material storage was uneven in the nugget area as well as joint interface line. The welded region appeared with projected materials as burrs for all magnification levels. Molded material was stored in the joint interface line as wave form with lot of blow holes. When this experimental run was compared with other experimental run, it had very worst condition as well as very poor responses. The main effects of each control factor on S/N ratio of average of all S/N ratios of responses are given in Fig. 3 and that shows the response graph for the designed process parameters. The response graph was obtained from the average value of S/N ratio of all S/N ratios for each level of the process parameter to select the composition of optimal factors. The optimal

5 LENIN et al.: Process parameters optimization for friction stir welding of Polypropylene 373 processing parameter setting selects the level with higher value of average of S/N ratio for each operating factors. Table 4 Comparison of the optimal results and confirmation experiment results in percentage S.No Input parameters Output parameters in percentage (%) X1 X2 X3 Tensile strength MN/m 2 Shore-D hardness Rockwell hardness Izod strength MN/m Charpy strength MN/m Nugget area mm 2 A A Error % Note: X1- Pin profile, X2- Feed rate mm/min, X3- Spindle speed, A1- Optimized results, and A2- Confirmation test Fig. 4 SEM images for confirmation tests Analysis of variance The purpose of the analysis of the variance is to investigate which friction stir welding process parameters significantly affects the quality characteristic and separates the total variability into contributes 21. In addition, the F test named Fisher 22 can also be used to determine which welding process parameters have a significant effect on the quality characteristic. Results of ANOVA (Table 3) indicate that tool pin profile, feed rate, and tool rotational speed are significant FSW process parameters affecting the multiple quality characteristics. The percentage contributions due to these process parameters are shown in Table 4. Based on the above discussion, the FSW process parameters with the optimal weld geometry are tool pin profile at level 4 (Threaded tool pin profile), feed rate at level 4 (60 mm/min), tool rotational speed at level 1 (1500 rpm). Confirmation experiment The optimized results are tabulated in Table 4. The confirmation test was carried out to validate the optimized results and their values are presented in Table 4 and Fig. 4. The error percentage of Izod strength and the nugget area changed around 15 %. The nugget area was finalized based on the heat distribution in the joint interface line. Even though the nugget area had high value, it did not produce good strength. From Fig. 4, it can be seen that the SEM images have few defects such as blow holes, cavities and cracks in the welded region. Conclusion In this paper, the selection of optimum process parameters for friction stir welding of polypropylene material has been carried out using average of S/N ratio and ANOVA. The major results are summarized below: 1) Characteristics of the nugget area and average of S/N ratio can be used for optimizing FSW process parameters and to find the corresponding optimum process parameters. 2) Based on the characteristics of the nugget area and average of S/N ratio, threaded pin profile at 4 mm/min feed rate and 2250 rpm tool rotational speed produced better effects in FSW of polypropylene material. 3) The process parameters such as tool pin profile,

6 374 J SCI IND RES VOL 73 JUNE 2014 feed rate and tool rotational speed are the most significant process parameters. The percentage of contribution of FSW process parameters was evaluated. It is found that the tool pin profile has 50% contribution, feed rate has 25% contribution and tool rotational speed has 18% contribution to average of S/N ratio. 4) The optimum value of process parameters such as tool pin profile, feed rate and tool rotational speed are found to be threaded pin profile, 60 mm/min, 1500 rpm respectively for maximizing the obtained results among the sixteen experiments. 5) Confirmation test was carried out and the test results were very close to the optimized results. References 1 Colligan K, Material flow behaviour during friction stir welding of aluminum, Welding J, 7 (1997) Thomas W M, Nicholas E D, Needham J C, Murch M G, Temple-smith P & Dawes C J, Friction-stir butt welding, GB patent No , Int pat app No: PCT/GB92/02203, Liu H J, Fuji H, Maeda M & Nogi K, Mechanical properties of friction stir welded joints of 1050-H24 aluminum alloy, Sci Technol Welding and Joining, 8 (2003) Barcellona A, Buffa G, Fratini L & Palmeri D, On microstructural phenomena occurring in friction stir welding of aluminum alloy, Mater Process Technol, 177 (2006) Elangovan K & Balasubramanian V, Influences of pin profile and rotational speed of the tool on the formation of friction stir processing zone in AA2219 aluminum alloy, J Mater Sci Engine, A459 (2007) Elangovan K & Balasubramanian V, Effects of tool pin pofile and axial force on the formation of friction stir processing zone in AA6061 aluminum alloy, Int J Adv Manufact Technol, 34 (2009) Maier, Clive, Teresa C, & Polypropylene, The definitive user s guide and databook: William Andrew, Morris P J T, Polymer Pioneers: A popular history of the science and technology of large molecules: Chemical Heritage Foundation, Juang S C, Tarng Y S & Lii H R, A comparison between the back-propagation and counter propagation networks in the modeling of the TIG welding process, J MaterProcess Technol, 75 (1998) Bendell A, Disney J,. ProdmoreW A, Taguchi methods: Applications in world industry, IFS Publications: UK (1989). 11 Sathiya P, Abdul J M Y, Katherasan D & Shanmugarajan B, Optimization of laser butt welding parameters with multiple performance characteristics. Optics & laser Technol, 43 (2011) Pan L K, Wang C C, Hsiao Y C & Ho K C, Optimization of Nd: YAG laser welding onto magnesium alloy via Taguchi analysis, Optics & Laser Technol, 37 (2004) Gugaraja S, Nooral H A & Karuppannan K M, Optimization of green sand casting process parameters by using Taguchi s method, Int J Adv Manufact Technol, 30 (2006) Kumar S, Kumar P & Shan H S, Parametric optimization of surface roughness castings produced by evaporative pattern casting process, Materials Lett, 60 (2006) Casalino G, Curcio F, Memola F & Minutolo C, Investigation on Ti6A14V laser welding using statistical and Taguchi approaches, J Mater Process Technol, 167 (2005) Montgomery D C, Design and Analysis of Experiments. 4 th Edition, NY: john-wiley & sons, Inc, Lakshminarayanan A K & Balasubramanian V, Process parameters optimization for friction stir welding of RDE-40 aluminum alloy using Taguchi technique, Transact Nonferrous Metals Soc China, 18 (2000) Chou D S, Pan L K & Chang B D, Optimization for solidification of low-level-radioactive resin using Taguchi analysis,waste Manage, 21 (2001) Dongxia Y, Xiaoyan L, dingyong H, Zuoren N & Hui H, Optimization of weld bead geometry in laser welding with filler wire process using Taguchi s approach, Optics & Laser Technol, 44 (2012) Phadke M S, Quality Engineering using Robust Design. Englewood Cliffs, NJ: Prentice Hall, Juang S C & Tang Y S, Process parameter selection for optimizing the weld pool geometry in the tungsten inert gas welding of stainless steel, J Mater Process Technol, 160 (2005) Fisher R A, Statistical methods for research worker, London: Oliver & Boyd, Panneerselvam K & Lenin K, Joining of Nylon 6 plate by friction stir welding process using threaded pin profile, J Materials and Design, 53 (2014) Panneerselvam K & Lenin K, Investigation on effect of tool forces and joint defects during FSW of polypropylene plate, Proc Engine, 38 (2012)