Abstract
With the use of ethanol blended fuel, secondary coating on steel becomes crucial to enhance the corrosion resistance in fuel tanks. Resistance seam welding is extensively used in the manufacturing of leakproof joints. An additional layer over the hot dip galvannealed steel increases electrical resistance. This requires the selection of a suitable welding parameter to achieve the desired nugget diameter and tensile strength. However, assessing the quality of resistance seam weld for a new coating is time-consuming and results in significant material wastage. Consequently, there is a need to optimize the welding process to enhance weld quality while saving both time and materials. An artificial neural network was utilized to predict weld quality by establishing a correlation between critical welding parameters such as welding current, welding speed, welding time, and cooling time on the nugget diameter and the failure type.Additionally, a mathematical model developed through multivariate regression analysis establishes a correlation between welding parameters and weld quality indicators. Optimization is also done using the genetic algorithm to achieve consistent and predictable weld quality with an accuracy greater than 98%. The outcomes of the optimization were subsequently validated through repeatability trials.
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References
Vates, U.K., Sharma, B.P., Kanu, N.J., Daniel, N.A., Subramanian, S., Sharma, P.: Optimization of Process Parameters of Galvanizing Steel in Resistance Seam Welding Using RSM, in: S. Yadav, D.B. Singh, P.K. Arora, H. Kumar (Eds.), Proceedings of International Conference in Mechanical and Energy Technology: ICMET 2019, India, Springer Singapore, Singapore, pp. 695–706. (2020)
Khosravi, A., Halvaee, A., Hasannia, M.H.: Weldability of electrogalvanized versus galvanized interstitial free steel sheets by resistance seam welding. Mater. Design. 44, 90–98 (2013)
Gholami, O., Shakeri, M., Imen, S.J., Jamshidi, H., Aval: Small-scale resistance seam welding of 304 stainless steel with capacitor discharge welding machine, Proceedings of the Institution of Mechanical Engineers, Part B: Journal of Engineering Manufacture 235(6–7) 1154–1167. (2021)
Koley, S., Akhtar, M.T., Kumar, N., Kundu, A., Shome, M.: Effect of secondary coating on Weldability, Joint Performance, and Electrode Life in Resistance Seam Welding of Galvannealed interstitial Free Steel. J. Mater. Eng. Perform. 31(3), 2432–2444 (2022)
Pouranvari, M., Asgari, H., Mosavizadch, S., Marashi, P., Goodarzi, M.: Effect of Weld nugget size on overload failure mode of resistance spot welds. Sci. Technol. Weld. Join. 12, 217–225 (2007)
Wohner, M., Mitzschke, N., Jüttner, S.: Resistance spot welding with variable electrode force—development and benefit of a force profile to extend the weldability of 22MnB5 + AS150. Weld. World. 65(1), 105–117 (2021)
Mira-Aguiar, T., Leitão, C., Rodrigues, D.M.: Solid-state resistance seam welding of galvanized steel. Int. J. Adv. Manuf. Technol. 86(5), 1385–1391 (2016)
Blom, A.H., Richardson, I.M., Elzinga, E., de Haas, M.: Resistance (mash) seam welding: Influence of welding conditions on tin distribution. Sci. Technol. Weld. Joining. 13(1), 1–9 (2008)
Jo, D.-H., Yun, S.-M., Park, K.-C., Kim, M.-S., Kim, J.-S.: Excellent Seam Weldable Nano-Composite Coated Zn-Ni plating steels for Automotive Fuel Tank. Corros. Sci. Technol. 18(1), 16–23 (2019)
Tumuluru, M.: The effect of coatings on the resistance spot welding behavior of 780 MPa dual-phase steel. Weld. J. 86, 161s–169 (2007)
Hu, X., Zou, G., Dong, S., Lee, M., Jung, J., Zhou, Y.: Effects of Steel Coatings on Electrode Life in Resistance Spot Welding of Galvannealed Steel sheets. Mater. Trans. 51, 2236–2242 (2010)
da Silva, R.F., Vieira, S.L.: Influence of the coating type on electrode life in spot weldingArticle based on a version presented at the XXXII CONSOLDA, Belo Horizonte, Minas Gerais, Brazil, 2–5 October 2006. Weld. Int. 23(3), 186–192 (2009)
Matsuda, H., Matsuda, Y., Kabasawa, M.: Effects of aluminium in the Zn coating on electrode life in welding galvanized steel sheet. Weld. Int. 10(8), 605–613 (1996)
He, Y., Yang, K., Wang, X., Huang, H., Chen, J.: Quality Prediction and Parameter Optimisation of Resistance Spot Welding using machine learning. Appl. Sci. 12(19), 9625 (2022)
Johnson, N.N., Madhavadas, V., Asati, B., Giri, A., Hanumant, S.A., Shajan, N., Arora, K.S., Selvaraj, S.K.: Implementation of machine learning algorithms for Weld Quality Prediction and optimization in Resistance Spot Welding. J. Mater. Eng. Perform. (2023)
Johnson, N.N., Madhavadas, V., Asati, B., Giri, A., Hanumant, S.A., Shajan, N., Arora, K.S.: Multi-objective optimization of Resistance Spot Welding parameters of BH340 Steel using kriging and NSGA-III. Trans. Indian Inst. Met. 76(11), 3007–3020 (2023)
Zamanzad Gavidel, S., Lu, S., Rickli, J.L.: Performance analysis and comparison of machine learning algorithms for predicting nugget width of resistance spot welding joints. Int. J. Adv. Manuf. Technol. 105(9), 3779–3796 (2019)
Zhu, X.-K., Zhu, J.B., Zhang, W.: Data-driven models of dynamic strength of resistance spot welds in high strength steels by regression and machine learning, Multiscale and Multidisciplinary modeling. Experiments Des. 5(4), 337–350 (2022)
Amiri, N., Farrahi, G.H., Kashyzadeh, K.R., Chizari, M.: Applications of ultrasonic testing and machine learning methods to predict the static & fatigue behavior of spot-welded joints. J. Manuf. Process. 52, 26–34 (2020)
Chen, G., Sheng, B., Luo, R., Jia, P.: A parallel strategy for predicting the quality of welded joints in automotive bodies based on machine learning. J. Manuf. Syst. 62, 636–649 (2022)
Dai, W., Li, D., Zheng, Y., Wang, D., Tang, D., Wang, H., Peng, Y.: Online quality inspection of resistance spot welding for automotive production lines. J. Manuf. Syst. 63, 354–369 (2022)
Bhadeshia, H.: Phase Transformations during Spot Welding of Interstitial – Free Steel, (2010)
Mukhopadhyay, G., Bhattacharya, S., Ray, K.K.: Strength assessment of spot-welded sheets of interstitial free steels. J. Mater. Process. Technol. 209(4), 1995–2007 (2009)
Rao, S.S., Chhibber, R., Arora, K.S., Shome, M.: Resistance spot welding of galvannealed high strength interstitial free steel. J. Mater. Process. Technol. 246, 252–261 (2017)
Salimi Beni, S., Atapour, M., Salmani, M.R., Ashiri, R.: Resistance Spot Welding Metallurgy of Thin sheets of Zinc-Coated interstitial-free steel. Metall. Mater. Trans. A. 50(5), 2218–2234 (2019)
Shi, J., Zhu, Y., Khan, F., Chen, G.: Application of bayesian regularization Artificial neural network in explosion risk analysis of fixed offshore platform. J. Loss Prev. Process Ind. 57, 131–141 (2019)
Ticknor, J.L.: A bayesian regularized artificial neural network for stock market forecasting. Expert Syst. Appl. 40(14), 5501–5506 (2013)
Sun, Z., Chen, Y., Li, X., Qin, X., Wang, H.: A bayesian regularized artificial neural network for adaptive optics forecasting. Opt. Commun. 382, 519–527 (2017)
Hirschen, K., Schäfer, M.: Bayesian regularization neural networks for optimizing fluid flow processes. Comput. Methods Appl. Mech. Eng. 195(7), 481–500 (2006)
Mirjalili, S., Algorithm, G.: Evolutionary Algorithms and Neural Networks: Theory and Applications, pp. 43–55. Springer International Publishing, Cham (2019)
Elsayed, S.M., Sarker, R.A., Essam, D.L.: A new genetic algorithm for solving optimization problems. Eng. Appl. Artif. Intell. 27, 57–69 (2014)
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Shajan, N., Johnson, N.N., Asati, B. et al. Optimization and prediction of resistance seam weld quality in secondary coated steels using machine learning. Int J Interact Des Manuf (2024). https://doi.org/10.1007/s12008-024-01989-7
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DOI: https://doi.org/10.1007/s12008-024-01989-7