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Taguchi Tabanlı Gri İlişkisel Analiz Kullanılarak Sürtünme Karıştırma Nokta Kaynak İşleminin Çoklu Yanıt Optimizasyonu

Year 2021, Volume: 36 Issue: 2, 421 - 432, 16.08.2021
https://doi.org/10.21605/cukurovaumfd.982797

Abstract

Sürtünme karıştırma nokta kaynağı (SKNK), esasen demir dışı metalleri ve alaşımlarını birleştirmek için kullanılan katı hal kaynak yöntemidir. Diğer kaynak işlemlerinin aksine SKNK, kirlilikten arındırma ve dolgu malzemesine sahip olmama avantajlarına sahiptir. Bu çalışmada, Taguchi yöntemi ve Gri ilişkisel analiz (GİA) kullanılarak sürtünme karıştırma noktası kaynaklı EN AW 5005 alüminyum alaşımının çoklu yanıt optimizasyonu için bir girişimde bulunulmuştur. Girdi parametreleri olarak pim yüksekliği (h, mm), takım dönüşü (S, rpm) ve kaynak süresi (t, s) kullanılırken, çoklu yanıt olarak çekme kesme dayanımı (ÇKD, MPa) ve bağlantı performansı (% BP) kullanılmıştır. Bu nedenle, deneyleri planlamak amacıyla Taguchi’nin L8 ortogonal tasarım matrisi kullanılmıştır. Daha sonra daha yüksek gri ilişkisel dereceyi veren optimum koşulu belirlemek için gri ilişkisel analiz uygulanmıştır. En önemli parametrenin gösterilmesi için Varyans Analizi (ANOVA) yöntemi de yapılmıştır. Son olarak, sonuçları doğrulamak ve bu yöntem boyunca gri ilişkisel derecedeki gelişmeyi belirlemek için doğrulama testi uygulanmıştır. 122,16 MPa çekme kesme dayanımı ve 111,05 bağlantı verimi ile en iyi sonuçlar, 2,6 mm pim yüksekliği, 1500 rpm takım dönüşü ve 10 s kaynak süresi gibi parametrelerde elde edilmiştir. Gri ilişki derecesinde optimal parametrede, 0,310’luk önemli bir iyileşme elde edildi.

References

  • 1. Yang, Q., Mironov, S., Sato, Y.S., Okamoto, K., 2010. Material Flow During Friction Stir Spot Welding, Material Science and Engineering: A, 527(16-17), 4389-4398. doi:10.1016/j.msea.2010.03.082.
  • 2. Muci-Kuchler, K.H., Itapu, S.K., Arbegast, W.J., Koch, K.J., 2005. Visualization of Material Flow in Friction Stir Spot Welding, SAE International, 3323. https://doi.org/10.4271/2005-01-3323
  • 3. Kulekci, M.K., Esme, U., Er, O., Kazancoglu, Y., 2011. Modeling and Prediction of Weld Shear Strength in Friction Stir Spot Welding Using Design of Experiments and Neural Network, 42(11), 990-995. Doi: 10.1002/mawe.201100781.
  • 4. Smith, C.B., 2000. Robotic Friction Stir Welding Using a Standard Industrial Robot, Tower Automotive, Milwaukee, 1–12.
  • 5. Cam, G., Gucluer, S., Cakan, A., Serindag, H.T., 2009. Mechanical Properties of Friction Stir Butt-Welded Al-5086 H32 Plate, Materialwissenschaft und Werkstofftechnik 40(8):638-642, doi: 10.1002/mawe.200800455
  • 6. Badarinarayan, H., Shi, Y., Li, X., Okamoto, K., 2009. Effect of Tool Geometry on Hook Formation and Static Strength of Friction Stir Spot Welded Aluminium 5754-0 sheets, International Journal of Machine Tools&Manufacture, 49(11), 814–823. Doi: 10.1016/j.ijmachtools.2009.06.001.
  • 7. Kumar, C.L., Jayakumar, V., Bharathiraja, G., 2019. Optimization of Welding Parameters for Friction Stir Spot Welding of AA6062 with Similar and Dissimilar Thicknesses, Materials Today: Proceedings, 19(2), 251–255. https://doi.org/10.1016/j.matpr.2019.07.204.
  • 8. Jambhale, S., Kumar, S., Kumar, S., 2020. Multi-response Optimization of Friction Stir Spot Welded Joint with Grey Relational Analysis, Materials Today: Proceedings, 27(1), 1900–1908. https://doi.org/10.1016/j.matpr.2020.03.830
  • 9. Raj Kumar, P., Nandhakumar, S., Seenivasan, S., Chandraprakash, R., 2021. Parametric Optimizationof Friction Stir Spot Welded Aluminium AA6063 Alloy Joints, Materials Today: Proceedings, 37(2), 2897–2902. https://doi.org/10.1016/j.matpr.2020.08.667
  • 10. Abbass, M.K., Hussein, S.K., Kudair, A.A., 2021. Optimization and Characterization of Friction Stir Spot Welding of Aluminum Alloy (AA 5754-H114) with Pure Copper Sheet, IOP Conf. Series: Materials Science and Engineering, 1094, 1-13. doi:10.1088/1757- 899X/1094/1/012054
  • 11. Castro, C.C., Plaine, A.H., Alcântara, N.G., Santos, J.F., 2018. Taguchi Approach for the Optimization of Refill Friction Stir Spot Welding Parameters for AA2198-T8 Aluminum Alloy, The International Journal of Advanced Manufacturing Technology, 99, 1927–1936. https://doi.org/10.1007/s00170- 018-2609-2.
  • 12. Ibrahim, I.J., Yapici, G.G., 2019. Optimization of the Intermediate Layer Friction Stir Spot Welding Process. The International Journal of Advanced Manufacturing Technology, 104(1- 4), 993–1004. https://doi.org/10.1007/s00170- 019-03952-3
  • 13. Zuo, Y., Kong, L., Liu, Z., Lv, Z., Wen, H., 2020. Process Parameters Optimization of Refill Friction Stir Spot Welded Al/Cu Joint by Response Surface Method. Trans Indian Inst Met., 73(12), 2975–2984. https://doi.org/10.1007/s12666-020-02100-w
  • 14. Santana, L.M., Suhuddin, U.F.H., Ölscher, M.H., Strohaecker, T.R., dos Santos, J.F., 2017. Process Optimization and Microstructure Analysis in Refill Friction Stir Spot Welding of 3-mm-thick Al-Mg-Si Aluminum Alloy, Int J Adv Manuf Technol, 92, 4213–4220. Doi 10.1007/s00170-017-0432-9
  • 15. Suresh, S., Venkatesan, K., Natarajan, E., Rajesh, S., 2020. Performance Analysis of Nano Silicon Carbide Reinforced Swept Friction Stir Spot Weld Joint in AA6061-T6 Alloy. Silicon, 1-14. https://doi.org/10.1007/s12633-020-00751-4.
  • 16. Suryanarayanan, R., Sridhar, V.G., 2020. Effect of Process Parameters in Pinless Friction Stir Spot Welding of Al 5754-Al 6061 Alloys, Metallography, Microstructure, and Analysis, 9, 261–272. https://doi.org/10.1007/s13632-020-00626-5.
  • 17. Yanar, N., 2008. Using Taguchi Method Specifying the Parameters that Affects Surface Roughness in Hydraulic Cylinder Manufacturing, MSc Thesis, Selcuk University Graduate School of Natural And Applied Sciences Department of Industrial Engineering, 74.
  • 18. Sirvanci, M., 2011. Experiment Design for Quality. 112.
  • 19. Datta, S., Bandyopadhyay, A., Kumar, P.P., 2008. Grey-Based Taguchi Method for Optimization of Bead Geometry in Submerged Arc Bead-on-Plate Welding, International Journal of Advanced Manufacturing Technology, 39, 1136-1143. https://doi.org/10.1007/s00170-007-1283-6
  • 20. Meran C., 2006. The Joint Properties of Brass Plates by Friction Stir Welding, Materials and Design, 27(9), 719-726. https://doi.org/ 10.1016/j.matdes.2005.05.006
  • 21. Kumar Sahu, P., Kumari, K., Pal, S., Pal, S.K., 2016. Hybrid Fuzzy-grey-taguchi Based Multi Weld Quality Optimization of Al/Cu Dissimilar Friction Stir Welded Joints, Advances in Manufacturing, 4(3), 237–247. http://dx.doi.org/10.1007/s40436-016-0151-8
  • 22. Sun, S.J., Kim, J.S., Lee, W.G., Lim, J.Y., Go, Y., Kim, Y.M., 2017. Influence of Friction Stir Welding on Mechanical Properties of Butt Joints of AZ61 Magnesium Alloy, Advances in Materials Science and Engineering, 1-13. https://doi.org/10.1155/2017/7381403.
  • 23. Shaik, B., Gowd, H.G., Prasad, B.D., 2019. Investigations on Friction Stir Welding Process to Optimize the Multi Responses Using GRA Method. International Journal of Mechanical Engineering and Technology, 10(3), 341–352.
  • 24. Prasath, S., Vijayan, S., Elil, R.D., 2020. Multi Response Optimization of Friction Stir Welding Process Parameters on Dissimilar Magnesium Alloys AZ31 and ZM21 using Taguchi-Based Grey Relation Analysis, La Metallurgia Italiana, 8, 18-27.
  • 25. Palani, K., Elanchezhian, C., 2018. Multi response Optimization of Friction Stir Welding Process Parameters in Dissimilar Alloys Using Grey Relational Analysis, the 3rd International Conference on Materials and Manufacturing Engineering, 390, 1-8. doi:10.1088/1757- 899X/390/1/012061.
  • 26. Vijayan, S., Raju, R., Rao, S.R.K., 2010. Multiobjective Optimization of Friction Stir Welding Process Parameters on Aluminum Alloy AA5083 Using Taguchi-based Grey Relation Analysis, Materials and Manufacturing Processes, 25, 1206-1212. https://doi.org/10.1080/10426910903536782
  • 27. Gupta, S.K., Pandey, K.N., Kumar, R., 2014. Multi-Objective Optimization of Friction Stir Welding of Aluminium Alloy Using Grey Relation Analysis with Entropy Measurement Method, Nirma Univeristy Journal of Engineering and Technology, 3(1), 29-34. https://doi.org/10.1177/1464420715627294
  • 28. Babu, K.K., Panneerselvam, K., Sathiya, P., Haq, A.N., Sundarrajan, S., Mastanaiah, P., Srinivasa Murthy, C.V., 2018. Parameter Optimization of Friction Stir Welding of Cryorolled AA2219 Alloy Using Artificial Neural Network Modeling with Genetic Algorithm, International Journal of Advanced Manufacturing Technology, 94, 3117-3129. https://doi.org/10.1007/s00170-017-0897-6.
  • 29. Yunus, M., Alsoufi, M.S., 2018. Mathematical Modelling of a Friction Stir Welding Process to Predict the Joint Strength of Two Dissimilar Aluminium Alloys Using Experimental Data and Genetic Programming, Modelling and Simulation in Engineering, 1-18. https://doi.org/10.1155/2018/4183816.
  • 30. Yousif, Y.K., Daws, K.M., Kazem, B.I., 2008. Prediction of Friction Stir Welding Characteristic Using Neural Network, Jordan Journal of Mechanical and Industrial Engineering, 2(3), 151-155.

Multi Response Optimization of Friction Stir Spot Welding Process

Year 2021, Volume: 36 Issue: 2, 421 - 432, 16.08.2021
https://doi.org/10.21605/cukurovaumfd.982797

Abstract

Friction stir spot welding (FSSW) is a solid state welding method mainly used to join non-ferrous metals and their alloys. When opposed to other welding processes, FSSW has the benefits of being pollution-free and having no filler material. In this study an attempt was made for multi response optimization of friction stir spot welded EN AW 5005 aluminum alloy using Taguchi method and Grey relational analysis (GRA). Pin height (h, mm), tool rotation (S, rpm), and welding time (t, s) were used as input parameters while tensile shear strength (TSS, MPa) and joint efficiency (JE, %) were used as multi response parameters. Therefore, Taguchi’s L8 orthogonal design matrix was used in order to plan the experiments. GRA was then applied to determine the optimum condition which gives the higher grey relational degree. Analysis of Variance method (ANOVA) was also carried out in order to show the most significant parameter. Finally, confirmation test was applied to confirm the results and determine the improvement in grey relational grade throughout this method. The best results were obtained with parameters such as
2.6 mm pin height, 1500 rpm tool rotation and 10 s welding time with 122.16 MPa TSS and 111.05 JE. A significant improvement of 0.310 was obtained in the optimal parameter in grey relation grade.

References

  • 1. Yang, Q., Mironov, S., Sato, Y.S., Okamoto, K., 2010. Material Flow During Friction Stir Spot Welding, Material Science and Engineering: A, 527(16-17), 4389-4398. doi:10.1016/j.msea.2010.03.082.
  • 2. Muci-Kuchler, K.H., Itapu, S.K., Arbegast, W.J., Koch, K.J., 2005. Visualization of Material Flow in Friction Stir Spot Welding, SAE International, 3323. https://doi.org/10.4271/2005-01-3323
  • 3. Kulekci, M.K., Esme, U., Er, O., Kazancoglu, Y., 2011. Modeling and Prediction of Weld Shear Strength in Friction Stir Spot Welding Using Design of Experiments and Neural Network, 42(11), 990-995. Doi: 10.1002/mawe.201100781.
  • 4. Smith, C.B., 2000. Robotic Friction Stir Welding Using a Standard Industrial Robot, Tower Automotive, Milwaukee, 1–12.
  • 5. Cam, G., Gucluer, S., Cakan, A., Serindag, H.T., 2009. Mechanical Properties of Friction Stir Butt-Welded Al-5086 H32 Plate, Materialwissenschaft und Werkstofftechnik 40(8):638-642, doi: 10.1002/mawe.200800455
  • 6. Badarinarayan, H., Shi, Y., Li, X., Okamoto, K., 2009. Effect of Tool Geometry on Hook Formation and Static Strength of Friction Stir Spot Welded Aluminium 5754-0 sheets, International Journal of Machine Tools&Manufacture, 49(11), 814–823. Doi: 10.1016/j.ijmachtools.2009.06.001.
  • 7. Kumar, C.L., Jayakumar, V., Bharathiraja, G., 2019. Optimization of Welding Parameters for Friction Stir Spot Welding of AA6062 with Similar and Dissimilar Thicknesses, Materials Today: Proceedings, 19(2), 251–255. https://doi.org/10.1016/j.matpr.2019.07.204.
  • 8. Jambhale, S., Kumar, S., Kumar, S., 2020. Multi-response Optimization of Friction Stir Spot Welded Joint with Grey Relational Analysis, Materials Today: Proceedings, 27(1), 1900–1908. https://doi.org/10.1016/j.matpr.2020.03.830
  • 9. Raj Kumar, P., Nandhakumar, S., Seenivasan, S., Chandraprakash, R., 2021. Parametric Optimizationof Friction Stir Spot Welded Aluminium AA6063 Alloy Joints, Materials Today: Proceedings, 37(2), 2897–2902. https://doi.org/10.1016/j.matpr.2020.08.667
  • 10. Abbass, M.K., Hussein, S.K., Kudair, A.A., 2021. Optimization and Characterization of Friction Stir Spot Welding of Aluminum Alloy (AA 5754-H114) with Pure Copper Sheet, IOP Conf. Series: Materials Science and Engineering, 1094, 1-13. doi:10.1088/1757- 899X/1094/1/012054
  • 11. Castro, C.C., Plaine, A.H., Alcântara, N.G., Santos, J.F., 2018. Taguchi Approach for the Optimization of Refill Friction Stir Spot Welding Parameters for AA2198-T8 Aluminum Alloy, The International Journal of Advanced Manufacturing Technology, 99, 1927–1936. https://doi.org/10.1007/s00170- 018-2609-2.
  • 12. Ibrahim, I.J., Yapici, G.G., 2019. Optimization of the Intermediate Layer Friction Stir Spot Welding Process. The International Journal of Advanced Manufacturing Technology, 104(1- 4), 993–1004. https://doi.org/10.1007/s00170- 019-03952-3
  • 13. Zuo, Y., Kong, L., Liu, Z., Lv, Z., Wen, H., 2020. Process Parameters Optimization of Refill Friction Stir Spot Welded Al/Cu Joint by Response Surface Method. Trans Indian Inst Met., 73(12), 2975–2984. https://doi.org/10.1007/s12666-020-02100-w
  • 14. Santana, L.M., Suhuddin, U.F.H., Ölscher, M.H., Strohaecker, T.R., dos Santos, J.F., 2017. Process Optimization and Microstructure Analysis in Refill Friction Stir Spot Welding of 3-mm-thick Al-Mg-Si Aluminum Alloy, Int J Adv Manuf Technol, 92, 4213–4220. Doi 10.1007/s00170-017-0432-9
  • 15. Suresh, S., Venkatesan, K., Natarajan, E., Rajesh, S., 2020. Performance Analysis of Nano Silicon Carbide Reinforced Swept Friction Stir Spot Weld Joint in AA6061-T6 Alloy. Silicon, 1-14. https://doi.org/10.1007/s12633-020-00751-4.
  • 16. Suryanarayanan, R., Sridhar, V.G., 2020. Effect of Process Parameters in Pinless Friction Stir Spot Welding of Al 5754-Al 6061 Alloys, Metallography, Microstructure, and Analysis, 9, 261–272. https://doi.org/10.1007/s13632-020-00626-5.
  • 17. Yanar, N., 2008. Using Taguchi Method Specifying the Parameters that Affects Surface Roughness in Hydraulic Cylinder Manufacturing, MSc Thesis, Selcuk University Graduate School of Natural And Applied Sciences Department of Industrial Engineering, 74.
  • 18. Sirvanci, M., 2011. Experiment Design for Quality. 112.
  • 19. Datta, S., Bandyopadhyay, A., Kumar, P.P., 2008. Grey-Based Taguchi Method for Optimization of Bead Geometry in Submerged Arc Bead-on-Plate Welding, International Journal of Advanced Manufacturing Technology, 39, 1136-1143. https://doi.org/10.1007/s00170-007-1283-6
  • 20. Meran C., 2006. The Joint Properties of Brass Plates by Friction Stir Welding, Materials and Design, 27(9), 719-726. https://doi.org/ 10.1016/j.matdes.2005.05.006
  • 21. Kumar Sahu, P., Kumari, K., Pal, S., Pal, S.K., 2016. Hybrid Fuzzy-grey-taguchi Based Multi Weld Quality Optimization of Al/Cu Dissimilar Friction Stir Welded Joints, Advances in Manufacturing, 4(3), 237–247. http://dx.doi.org/10.1007/s40436-016-0151-8
  • 22. Sun, S.J., Kim, J.S., Lee, W.G., Lim, J.Y., Go, Y., Kim, Y.M., 2017. Influence of Friction Stir Welding on Mechanical Properties of Butt Joints of AZ61 Magnesium Alloy, Advances in Materials Science and Engineering, 1-13. https://doi.org/10.1155/2017/7381403.
  • 23. Shaik, B., Gowd, H.G., Prasad, B.D., 2019. Investigations on Friction Stir Welding Process to Optimize the Multi Responses Using GRA Method. International Journal of Mechanical Engineering and Technology, 10(3), 341–352.
  • 24. Prasath, S., Vijayan, S., Elil, R.D., 2020. Multi Response Optimization of Friction Stir Welding Process Parameters on Dissimilar Magnesium Alloys AZ31 and ZM21 using Taguchi-Based Grey Relation Analysis, La Metallurgia Italiana, 8, 18-27.
  • 25. Palani, K., Elanchezhian, C., 2018. Multi response Optimization of Friction Stir Welding Process Parameters in Dissimilar Alloys Using Grey Relational Analysis, the 3rd International Conference on Materials and Manufacturing Engineering, 390, 1-8. doi:10.1088/1757- 899X/390/1/012061.
  • 26. Vijayan, S., Raju, R., Rao, S.R.K., 2010. Multiobjective Optimization of Friction Stir Welding Process Parameters on Aluminum Alloy AA5083 Using Taguchi-based Grey Relation Analysis, Materials and Manufacturing Processes, 25, 1206-1212. https://doi.org/10.1080/10426910903536782
  • 27. Gupta, S.K., Pandey, K.N., Kumar, R., 2014. Multi-Objective Optimization of Friction Stir Welding of Aluminium Alloy Using Grey Relation Analysis with Entropy Measurement Method, Nirma Univeristy Journal of Engineering and Technology, 3(1), 29-34. https://doi.org/10.1177/1464420715627294
  • 28. Babu, K.K., Panneerselvam, K., Sathiya, P., Haq, A.N., Sundarrajan, S., Mastanaiah, P., Srinivasa Murthy, C.V., 2018. Parameter Optimization of Friction Stir Welding of Cryorolled AA2219 Alloy Using Artificial Neural Network Modeling with Genetic Algorithm, International Journal of Advanced Manufacturing Technology, 94, 3117-3129. https://doi.org/10.1007/s00170-017-0897-6.
  • 29. Yunus, M., Alsoufi, M.S., 2018. Mathematical Modelling of a Friction Stir Welding Process to Predict the Joint Strength of Two Dissimilar Aluminium Alloys Using Experimental Data and Genetic Programming, Modelling and Simulation in Engineering, 1-18. https://doi.org/10.1155/2018/4183816.
  • 30. Yousif, Y.K., Daws, K.M., Kazem, B.I., 2008. Prediction of Friction Stir Welding Characteristic Using Neural Network, Jordan Journal of Mechanical and Industrial Engineering, 2(3), 151-155.
There are 30 citations in total.

Details

Primary Language English
Subjects Engineering
Journal Section Articles
Authors

Onur Er This is me 0000-0003-2969-1071

Mustafa Kemal Külekci 0000-0002-5829-3489

Uğur Eşme 0000-0002-0672-7943

Cem Boğa

Publication Date August 16, 2021
Published in Issue Year 2021 Volume: 36 Issue: 2

Cite

APA Er, O., Külekci, M. K., Eşme, U., Boğa, C. (2021). Multi Response Optimization of Friction Stir Spot Welding Process. Çukurova Üniversitesi Mühendislik Fakültesi Dergisi, 36(2), 421-432. https://doi.org/10.21605/cukurovaumfd.982797