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Friction welding parameter for AA6063 using ANFIS prediction

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Abstract

It is generally accepted in the field of friction welding that the parameters of rotary friction welding affect mechanical properties. Accurate input is crucial at each process stage, especially in smart factories that use automated machines to produce workpieces. The input for each parameter must be precise. This research proposes a prediction parameter for rotary friction welding for aluminium round bar AA6063, which uses the adaptive-network-based fuzzy inference system (ANFIS) method. The condition, which includes rotational speed, welding time, and friction pressure was used to input the membership function. The ultimate tensile strength of the weld joint was used for the output of ANFIS. The results show that prediction can contribute to industrial applications by determining which parameters can be adapted to the application control for the automatic rotary friction welding process in a Smart Factory.

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References

  1. Srinivasan M, Loganathan C, Balasubramanian V, Nguyen QB, Gupta M, Narayanasamy R (2011) Feasibility of joining AZ31B magnesium metal matrix composite by friction welding. Mater Des 32(3):1672–1676

    Article  Google Scholar 

  2. Kar A, Kumar S, Kailas SV (2023) Developing multi-layered 3D printed homogenized structure using solid state deposition method. Mater Charact 199:112770

    Article  Google Scholar 

  3. Kar A, Pickron EJ, Curtis T, Jasthi BK, Lein W, McClelland Z, Crawford G (2023) Effect of friction stir processing on the microstructure and mechanical properties of thick Al-6061 alloy. In TMS Annual Meeting & Exhibition. Springer Nature Switzerland, Cham, p 51–62

  4. Kumar S, Kar A (2021) A review of solid-state additive manufacturing processes. Trans Indian Natl Acad Eng 6:955–973

    Article  Google Scholar 

  5. Vairis A, Papazafeiropoulos G, Tsainis AM (2016) A comparison between friction stir welding, linear friction welding and rotary friction welding. Adv Manuf 4(4):296–304

    Article  Google Scholar 

  6. Song YL, Liu YH, Zhu XY, Yu SR, Zhang YB (2008) Strength distribution at interface of rotary-friction-welded aluminum to nodular cast iron. Trans Nonferrous Met Soc China 18(1):14–18

    Article  Google Scholar 

  7. Li P, Sun H, Wang S, Hao X, Dong H (2020) Rotary friction welding of AlCoCrFeNi2. 1 eutectic high entropy alloy. J Alloys Compd 814:152322

    Article  Google Scholar 

  8. Stutz M, Buzolin R, Pixner F, Poletti C, Enzinger N (2019) Microstructure development of molybdenum during rotary friction welding. Mater Charact 151:506–518

    Article  Google Scholar 

  9. Jin F, Li J, Liu P, Nan X, Li X, Xiong J, Zhang F (2019) Friction coefficient model and joint formation in rotary friction welding. J Manuf Process 46:286–297

    Article  Google Scholar 

  10. Li X, Li J, Jin F, Xiong J, Zhang F (2018) Effect of rotation speed on friction behavior of rotary friction welding of AA6061-T6 aluminum alloy. Weld World 62(5):923–930

    Article  Google Scholar 

  11. Dong H, Yang J, Li Y, Xia Y, Hao X, Li P, ... & Lei M (2020) Evolution of interface and tensile properties in 5052 aluminum alloy/304 stainless steel rotary friction welded joint after post-weld heat treatment. J Manuf Process 51:142–150

  12. Kumar AS, Khadeer SA, Rajinikanth V, Pahari S, Kumar BR (2021) Evaluation of bond interface characteristics of rotary friction welded carbon steel to low alloy steel pipe joints. Mater Sci Eng A 824:141844

    Article  Google Scholar 

  13. Anandaraj A, Rajakumar S, Balasubramanian V, Kavitha S (2021) Influence of process parameters on hot tensile behavior of rotary friction welded In 718/AISI 410 dissimilar joints. CIRP J Manuf Sci Technol 35:830–838

    Article  Google Scholar 

  14. Mullo JL, Ramos-Grez JA, Barrionuevo GO (2021) Effect of laser heat treatment on the mechanical performance and microstructural evolution of AISI 1045 steel-2017-T4 aluminum alloy joints during rotary friction welding. J Mater Eng Perform 30(4):2617–2631

    Article  Google Scholar 

  15. Jantarasricha T, Kunhirunbawon S, Wimol M (2021) Investigation of optimum parameters for rotary friction welding of aluminum round bars using three-level factorial design methodology. J Met Mater Miner 31(4):88–94

    Google Scholar 

  16. Selvaraj R, Shanmugam K, Selvaraj P, Balasubramanian V (2023) Optimization of process parameters of rotary friction welding of low alloy steel tubes using response surface methodology. Forces Mech 10:100175

    Article  Google Scholar 

  17. Trembach B, Grin A, Makarenko N, Zharikov S, Trembach I, Markov O (2020) Influence of the core filler composition on the recovery of alloying elements during the self-shielded flux-cored arc welding. J Market Res 9(5):10520–10528

    Google Scholar 

  18. Trembach B, Grin A, Turchanin M, Makarenko N, Markov O, Trembach I (2021) Application of Taguchi method and ANOVA analysis for optimization of process parameters and exothermic addition (CuO-Al) introduction in the core filler during self-shielded flux-cored arc welding. Int J Adv Manuf Technol 114(3–4):1099–1118

    Article  Google Scholar 

  19. Trembach B, Balenko O, Davydov V, Brechko V, Trembach I, Kabatskyi O (2022) Prediction the melting characteristics of self-shielded flux cored arc welding (FCAW-S) with exothermic addition (CuO-Al). In 2022 IEEE 4th International Conference on Modern Electrical and Energy System (MEES). IEEE, p 01–06

  20. Chaudhary N, Singh S (2022) Experimental investigation and ANFIS-based modelling of effect of process parameters on friction stir spot welding of Al 6061-T6. Adv Mater Process Technol 1–11. https://doi.org/10.1080/2374068X.2022.2096833

  21. Mishra D, Prasad RS, Kumar S (2021) ANFIS model to predict effect of tool pin length and position on tensile strength of friction stir welded joint. Weld Int 35(1–3):24–33

    Article  Google Scholar 

  22. Maalekian M (2007) Friction welding–critical assessment of literature. Sci Technol Weld Joining 12(8):738–759

    Article  Google Scholar 

  23. Jang JS (1993) ANFIS: adaptive-network-based fuzzy inference system. IEEE Trans Syst Man Cybern 23(3):665–685

    Article  Google Scholar 

  24. Takagi T, Sugeno M (1983) Derivation of fuzzy control rules from human operator’s control actions. IFAC Proceedings 16(13):55–60

    Google Scholar 

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Acknowledgements

The authors are also thankful to the Department of Production Engineering Technology, Faculty of Industrial Technology, Pibulsongkram Rajabhat University for supporting the necessary resources in this research.

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Authors and Affiliations

  1. Department of Production Engineering Technology, Faculty of Industrial Technology, Pibulsongkram Rajabhat University, Plai Chumphon Subdistrict, Mueang Phitsanulok, Phitsanulok, Thailand

    Siridech Kunhirunbawon, Narisara Suwichien & Tanakorn Jantarasricha

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  1. Siridech Kunhirunbawon
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  2. Narisara Suwichien
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  3. Tanakorn Jantarasricha
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Contributions

All authors contributed to the study conception and design. Material preparation, data collection and analysis were performed by [Siridech Kunhirunbawon, Ph.D.], [Narisara Suwichien] and [Tanakorn Jantarasricha, Ph.D.]. The first draft of the manuscript was written by [Siridech Kunhirunbawon, Ph.D.] and all authors commented on previous versions of the manuscript. All authors read and approved the final manuscript.

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Correspondence to Tanakorn Jantarasricha.

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Kunhirunbawon, S., Suwichien, N. & Jantarasricha, T. Friction welding parameter for AA6063 using ANFIS prediction. Int J Adv Manuf Technol 128, 2589–2597 (2023). https://doi.org/10.1007/s00170-023-12106-5

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  • DOI: https://doi.org/10.1007/s00170-023-12106-5

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