M. SIVA PRAGASH. SAJI VARGHESE Research scholar Department of Mechanical Engineering Noorul Islam University,

Transcription

1 Volume No., Issue No., December January 04, M. SIVA PRAGASH RAMKUMAR SAJI VARGHESE Research scholar Department of Mechanical Engineering Department of Mechanical Engineering Department of Mechanical Engineering Noorul Islam University, and Technology Noorul Islam University, kumara coil, TamilNadu, India Yanbu Industrial College kumara coil, TamilNadu, India Kingdom of Saudi Arabia Abstract: Friction Stir Welding (FSW) is a solidstate welding technique used primarily for joining nonferrous metals and alloys. It has many advantages when compared to traditional methods, as it is pollution free and no consumable materials are involved. In this study, the FSW of AZ9D with AZB plates was carried out to optimize the process parameters. For the investigation, experiments were conducted using the Taguchi (L8) orthogonal array and to optimize the process parameters, grey based Taguchi techniques have been adopted. The Analysis of variance was used to find out the significant parameter that affects the mechanical properties. A second order model has been developed between the process parameters and performance characteristics using the response surface methodology. The predicted optimal conditions were validated by conducting a confirmation test. Key Words: Optimization, Taguchi Method, Grey Relation Analysis, Friction Stir Welding, Magnesium Alloy. I. INTRODUCTION The Friction Stir Welding process produces a joint that is stronger than the fusion arc welded joint. The Welding of light weight materials like aluminum, magnesium, titanium by friction stir welding has been widely used in many applications including aerospace, automotive and ship building industries, as well as in the military []. However, to expand the usage of magnesium alloys, more effective welding and joining techniques are required. As an emerging joining technique, Friction Stir Welding (FSW) has greater potential for magnesium alloys since it can significantly reduce weld defects such as oxide inclusions, porosity, cracks, and distortions, commonly encountered in fusion welded joints []. In Friction Stir Welding the process variables such as rotation speed, travel speed, tool geometry has a vital role to develop quality joints [] The tool speed and tool geometry must be considered in Friction Stir Welding, as both parameters have considerable importance and must be chosen with care to ensure a successful and efficient welding cycle [4]. The relationship between the welding speeds and the heat input during welding is complex but, in general, increasing the rotation speed or decreasing the traverse speed will result in a hotter weld. In order to produce a successful weld it is necessary that the material surrounding the tool should be hot enough to enable the extensive plastic flow required and minimize the forces acting on the tool. If the material is too cold, and then voids or other flaws may be present in the stir zone and in extreme cases, the tool may break [5]. Figure shows the tool and work piece arrangement in FSW. The tool rotation and welding speeds are imposed the changes on microstructure and mechanical characteristics of friction stir welded joints of A9 cast al alloy were effective [6]. AZ9D work piece Tool Rotation AZB work piece Tool shoulder Welding Direction Tool pin Fig. Tool and work pieces The author [7] studied the heat generation, heat transfer and plastic flow during friction stir welding, laying emphasis on the properties of the welded materials and formation of defects. [8] ISSN Optimized the process parameters in friction stir welding (FSW) of Aluminum Alloy AA 508, multiple responses based on orthogonal array with grey relational analysis. The L9 orthogonal array 0 All rights Reserved. Page 674

2 Volume No., Issue No., December January 04, Taguchi experimental design is used for optimizing the FSW process parameters on the tensile strength of FSW welds. The process parameters considered for optimization are the Rotational speed of the tool in rpm, the transverse speed in mm/min, and the axial force in KN. This study [9] focused on the multiresponse optimization of the friction stir welding (FSW) process for an optimal parametric combination to yield favorable tensile strength and elongation, using the Taguchi based Grey relational analysis (GRA). Here the FSW parameters are the rotating speed, the welding speed and the tool shoulder diameter; they were optimized during the experiments using Taguchi s L8 orthogonal array. [0 & ] used Taguchi statistical technique to identify significant factors by conducting relatively fever experiments. They adopted the Taguchi L9 method to analyze the effect of rotational speed, traverse speed and axial force on tensile strength of RDE40 aluminum alloy for the FSW process. [] has used the Taguchi grey relational analysis with L8 orthogonal method to optimize the tool parameters of friction stir welded joints of AZB. The Analysis of Variance (ANOVA) is used to identify the process parameters which are statistically significant[]. In this research, an attempt has been made to carry out the experiments based on L8 orthogonal array. In order to obtain a quality weld, the process parameters are optimized using the Taguchibased grey relation analysis for dissimilar magnesium welded joints of AZ9DAZB. II. OPTIMIZING PARAMETERS BY TAGUCHI BASED GREY RELATION METHOD Here to design a highquality system the Taguchi method is used as a powerful tool. It gives both an efficient and a systematic approach to optimize designs for better performance and quality [4]. The orthogonal array experimental design has minimized the number of experiments. The S/N ratio is used to determine the best performance of the process parameter which contributes to enhancing the quality characteristics. The S/N ratio performance characteristics are divided into three categories; smallerthebetter, nominalthebest and largerthebetter categories. In order to optimize the process parameters, the largerthebetter quality characteristic is selected to imize the tensile strength and hardness. The loss function for the higherthebetter performance characteristic can be expressed as Lij n n k y ij ISSN () However, to minimize the ductility lowerthebetter performance has been preferred. The loss function for the lowerthebetter performance characteristics can be expressed as ηij = (/n Σ yij ) () th where, Lij is the loss function of the i performance characteristic in the jth experiment, yijk the experimental value of the ith performance characteristic in the jth experiment at the kth trial, and n the number of trials. The loss function is further transformed into a S/N ratio. In the Taguchi method [5], the S/N ratio is used to determine the deviation of the performance characteristic from the desired value. The S/N ratio ηij for the ith performance characteristic in the jth experiment can be expressed as ηij = 0 log (Lij) () But the Taguchi method is applicable to optimize the single response characteristic problem. Therefore to optimize the multi response characteristic problem, the grey relation analysis is selected in this investigation [6]. The grey relation technique is used to analyze the relational grade for the discrete sequence. In this, the S/N ratio is first normalized into a range between zero and the unity [7]. The normalized S/N ratio χij for the ith performance characteristic in the jth experiment can be expressed as ij ij min ij (4) ij min ij Where χij is the value after the grey relational generation, min ηij is the smallest value ηji of the jth response, and ηij is the largest value of ηij for the jth response. Then the grey relation coefficient is determined by using the normalized value. The grey relational coefficient ζij for the ith performance characteristic in the jth experiment can be expressed as ij 0 mini minj i0 ij i j i ij (5) 0 i0 ij i j i ij where χ0i is the ideal normalized S/N ratio for the ith performance characteristic and ζ the distinguishing coefficient which is defined in the range 0 ζ. Next, averaging the grey relation coefficient gives the grey relation grade. The grey relational grade γj can be obtained, using the following. j n w m k i 0 All rights Reserved. (6) Page 675

3 Volume No., Issue No., December January 04, where γj is the grey relational grade for the jth experiment, wi the weighting factor for the ith performance characteristic, and m the number of performance characteristics. These results show that complex multiple performance characteristics can be converted into a single response grey relation grade. Table : Alloy Base material AZ9D Mg 90 III. EXPERIMENTAL SETUP Magnesium AZ9D and AZB sheets of 0 mm long, 60 mm wide and 6 mm thick were used in this study. The Chemical composition of the base material is given in Table. Base material chemical composition (weight in %) Al Mn Zn Si Cu Ni Fe Br Ca Others Base material AZB The friction stir welding process was carried out on a Vertical Milling machine as shown in fig. (a) (b) Fig. (a) Vertical Milling Machine with fixture and (b) Fixture Special clamping methods were used to have a firm grip of the work pieces to prevent them from slipping. The tensile strength, elongation and hardness tests were carried out using Zwick Ultimate Tensile Testing machine and hardness testing machine. The identified process parameters and their levels are given in Table. The L8 orthogonal array is selected as per standards suggested by the Taguchi approach [8] is shown in Table 4. Table Welding Parameters and their Levels Process Parameter/Level Symbols Low () Medium () High () Pin Length (PL) in mm PL Rotating Speed (RS) in rpm RS Welding Speed (WS) in inch/min WS..6 ISSN All rights Reserved. Page 676

4 Volume No., Issue No., December January 04, Table 4. L8 orthogonal array Ex. No PL RS WS Eighteen pieces were welded as per the specimens of AZ9D with AZB obtained experimental plan. Fig. shows the welded according to the L8 experiment condition. Fig. welded joints IV. RESULTS AND DISCUSSION (i) S/N Ratio Table 5. Experimental Result and S/N Ratio PL RS WS UTS N/mm ISSN Ductility Hardness S/N of Hardness S/N of 0 All rights Reserved. S/N of UTS Page 677

5 Volume No., Issue No., December January 04, The experimental results for tensile strength, ductility and hardness and the calculated S/N ratio are given in Table 5. It can be noted that parameter levels may be varied for different performance characterization. Hence these parameters cannot be (ii) Grey Relation Analysis optimized by the Taguchi S/N ratio. Therefore, a multi response characterization index is necessary for optimization. In this investigation, complex S/N values are used in the grey relation analysis for optimizing the multi objective problem. UTS Normali ze Table 6. Coefficie nt Normalized, Coefficient and grade of UTS, Ductility and Hardness Ductili Normali Coefficie Hardne Normali ty ze nt ss ze Coefficie nt Grey Grade ISSN All rights Reserved. Page 678

6 Volume No., Issue No., December January 04, Table. 6 shows the normalized S/N ratio and the grey relation coefficient for ultimate tensile strength, ductility and hardness. Generally, a larger normalized value exhibits the better performance and the best normalized value is equal to one[9]. The grey relation coefficient shows the relationship between the ideal and the measured value [0]. The grey relation grade for each experiment is Table 7. Response Table for Mean of grey grade Parameters MEAN MEAN MEAN PL RS WS Total Mean Grey 0.7 calculated and the results are given in Table 6. It is observed that experiment 5 shows the best multiple performance characteristics when compared with other 7 experiments. Table 7 shows the total mean grey relation grade and mean for each level of the process parameters. The optimal level of the three process parameters are illustrated in the Fig 5. ISSN All rights Reserved. Page 679

7 Volume No., Issue No., December January 04, Grey Relational Grade Level of Design Vriables WS RS PL Fig.5 Grey Relation Grade (iii) ANOVA Table.7 ANOVA for Grey Grade Parameters SSQ MEANSQ F P DOF SST PL RS WS ERROR TOTAL In this study, to investigate the significance of the process parameters on performance characteristics ANOVA was used. The results of ANOVA for Grey relation grade values are shown in Table 7. In this context, the parameter pin length plays a major role on the performance characteristics and followed by tool rotating speed. Optimal welding process parameters are pin length of 4.5 mm (level ), tool rotating speed of 750 rpm (level ), and welding speed of inch/mm and are shown in the analysis. (iv) % contribution Response Surface Regression The second order mathematical model has been developed so that the tensile strength, ductility and hardness can be expressed as the function of pin length, rotating speed and welding speed. The relationship between the performance ISSN characteristics and welding process parameters are expressed as follows: () UTS UTS= * WS * RS * PL 0.5* WS.6*04 *RS 0.05 * WS*RS * WS*PL * RS*PL () Ductility Ductility = * WS 0.07* RS.08* PL + 0.5* WS * 06 * RS * WS*RS + 0.5* WS*PL * RS*PL () 0 All rights Reserved. Page 680

8 Volume No., Issue No., December January 04, Hardness = * WS * RS.* PL 4.80* WS.89* 05 * RS 0.00* WS*RS + 4.5* WS*PL *0 *RS*PL V.. It was observed that a welding speed of inch/min, a rotational speed of 750 rpm and a pin length of 4.5 give the better results. The calculation of the grey relational grade helped to quantify the integrated performance in the pin length, welding speed and rotational speed on weld quality.. The ANOVA results emphasize that the parameter pin length contribution was 50.4 % and it has more influence on the quality performance of the weld. 4. The second order response surface model for performance characteristics have been developed for the observed data. The predicted and measured values close much close to each other, which shows that developed models are reliable for prediction. REFERENCES [] [] [] [4] ASM Handbook, Welding, brazing, and soldering, vol. 6, 99. [6] Mahmoud T. S., Gaafer A. M. and Khalifa T. A., Effect of tool rotational and welding speeds on microstructural and mechanical characteristics of friction stir welded A9 cast Al alloy, Materials Science and Technology, 0, 4, 5, [7] R. Nandan, T. DebRoy and H. K. D. H. Bhadeshia, Recent Advances in Friction Stir Welding Process, Weldment Structure and properties, Progress in Materials Science, 008, 5, [8] Vijayan S, Raju R. and Rao S.R.K., Multi objective optimization of Friction Stir Welding Process Parameters on Aluminium Alloy AA508 Using Taguchi Based Grey Relation Analysis 5, 06, 00. [9] Hakan Aydin, Ali Bayram, Ugur Esme, Yigit Kazancoglu and Onur Guven, Application of grey relation analysis (gray) and Taguchi method for the parametric optimization of friction stir welding (fsw) process, Materials and technology, 44, 4, 00, 05. [0] A. Varun Kumar and K. Balachandar, Effect of welding parameters on Metallurgical Properties of Friction Stir Welded Aluminium Alloy 6060, Journal of Applied Sciences, 0,,, [] Lakshminarayanan, A.K. and V. Balasubramanian, Process parameters optimization for friction stir welding of RDE40 aluminum alloy using Taguchi technique, Transactions of Nonferrous Metals Society of China, 8,, 008, [] A.Ramkumar, M.Sivapragash, Saji Varghese, Optimizing the Process Parameters of FSW CONCLUSION The grey based Taguchi method was applied in this investigation to optimize the welding process parameters of welding the AZ9D with AZB magnesium alloys. The results are summarized as follows:. [5] Dayong Kim, wonoh Lee, Juneheyung Kim, Chongmin Kim, and Formability of evaluation of friction stir welded 6T4 sheet with respect joining material direction. International Journal of Mechanical Science (00), doi:0.06/j.ijmecsci H.Zhang, S.B. Lin, L.Wu, J.C Feng, Sh.L.Ma, Defects formation procedure and mathematical model for defect free friction stir welding of magnesium alloy, Materials and Design, 7, 006, A.Ramkumar, M.Sivapragash, Saji Varghese, TaguchiGrey Based Optimization of Process Parameters on AZ9D Mg Alloy by FSW, American Journal of Scientific Research, Issue 87 March, 0, pp Z. Fan, G. Liu, Y. Wang, Microstructure and mechanical properties of rheodicast AZ9D Magnesium alloy, Journal of Material Science, 006 Volume 4, Issue, pp ISSN on AZB Mg Alloy by TaguchiGrey Method, MiddleEast Journal of Scientific Rearch 0, 5 (): 667. [] Shivani Daftardar, Laser assisted friction stir welding: finite volume method and Metaheuristic optimization, Maharashtra Institute of Technology, India, 009. [4] Lung Kwang Pan, Che Chung Wang, Shien Long Wei, Hai Feng Sher, Optimizing multiple quality characteristics via Taguchi methodbased Grey analysis, Journal of Materials Processing Technology, 8, 007, 0 All rights Reserved. Page 68

9 Volume No., Issue No., December January 04, [5] Tosun.N, Cogun.C and Tosun.G, A study on kerf and material removal rate in wire electrical discharge machining based on Taguchi method, Journal of Materials Processing Technology, 5, 004, 6. [6] Sathiya, P., and Abdul Jaleel M.Y, Greybased Taguchi method for optimization of bead geometry in laser beadonplate welding, Advances in Production Engineering & Management, 5, 4, 00, 54. [7] Tarng.Y.S, Juang.S.C and Chang.C.H, The use of greybased Taguchi methods to determine submerged arc welding process parameter in hard facing, Journal of Material Processing Technology, 8, 00, 6. [8] A. K. Lakshminarayanan, V. Balasubramanian, Comparison of RSM with ANN in predicting tensile strength of friction stir welded AA709 aluminium alloy joints, Transaction of Nonferrous Metals Society of China, 9,009, 98. [9] J. L. Deng, Introduction to Grey System, Journal of Grey System, (), 989, 4. [0] P. Narender Singh, K. Raghukandan, B. C. Pai, Optimization by Grey relation analysis of EDM parameters on machining Al0% Sic composites, Journal of Materials Processing and Technology, 5556, 004, ISSN All rights Reserved. Page 68