Improving RSW nugget diameter prediction method: unleashing the power of multi-fidelity neural networks and transfer learning

Abstract

This research presents an innovative approach to accurately predict the nugget diameter in resistance spot welding (RSW) by leveraging machine learning and transfer learning methods. Initially, low-fidelity (LF) data were obtained through finite element numerical simulation and design of experiments (DOEs) to train the LF machine learning model. Subsequently, high-fidelity (HF) data were collected from RSW process experiments and used to fine-tune the LF model by transfer learning techniques. The accuracy and generalization performance of the models were thoroughly validated. The results demonstrated that combining different fidelity datasets and employing transfer learning could significantly improve the prediction accuracy while minimize the costs associated with experimental trials, and provide an effective and valuable method for predicting critical process parameters in RSW.

Keywords

Resistance spot welding (RSW) / Nugget diameter prediction / Multi-fidelity neural networks / Transfer learning

References

[1.] Makoto K, Yoshimasa F, Kazuhiro O, et al. Estimation of tensile shear strength of spot welded joint of steel sheets. Q J Jpn Weld Soc, 1996, 14(4): 754-761.

[2.] Oikawa H, Murayama G, Hiwatashi S, et al. Resistance spot weldability of high strength steel sheets for automobiles and the quality assurance of joints. Weld World, 2007, 51(3): 7-18.

[3.] Pouranvari M, Asgari HR, Mosavizadch SM, et al. Effect of weld nugget size on overload failure mode of resistance spot welds. Sci Technol Weld Joi, 2007, 12(3): 217-225.

[4.] Sun DQ, Lang B, Sun DX, et al. Microstructures and mechanical properties of resistance spot welded magnesium alloy joints. Mater Sci Eng A, 2007, 460: 494-498.

[5.] Sim J, Kim KY. Hybrid nugget diameter prediction for resistance spot welding. Procedia Manuf, 2018, 17: 395-402.

[6.] Birada AK, Dabade BM. Optimization of resistance spot welding process parameters in dissimilar joint of MS and ASS 304 sheets. Mater Today Proc, 2020, 26(2): 1284-1288.

[7.] Luo Y, Li CT, Xu HB. Regression modeling and process analysis of resistance spot welding on galvanized steel sheet. Mater Des, 2009, 30(7): 2547-2555.

[8.] Pandya KS, Grolleau V, Roth CC, et al. Fracture response of resistance spot welded dual phase steel sheets: experiments and modeling. Int J Mech Sci, 2020, 187.

[9.] Kumar A, Ghosh GK, Panda S, et al. Numerical simulation of weld nugget in resistance spot welding process. Mater Today Proc, 2020, 27: 2958-2963.

[10.] Andersson O, Melander A. Prediction and verification of resistance spot welding results of ultra-high strength steels through FE simulations. Model Numer Simul Mater Sci, 2015, 5(1): 26-37.

[11.] Eisazadeh H, Hamedi M, Halvaee A. New parametric study of nugget size in resistance spot welding process using finite element method. Mater Des, 2010, 31(1): 149-157.

[12.] Hussein HK, Shareef IR, Zayer IA. Prediction of spot welding parameters using fuzzy logic controlling. Eastern-Eur J Enterp Technol, 2019, 5(2): 57-64.

[13.] Thongchai A, Kawin S, Phisut A, et al. Resistance spot welding optimization based on artificial neural network. Int J Manuf Eng, 2014, 2014.

[14.] Mallaradhya HM, Kumar MV, Chandra MSV. Optimization of parameters and prediction of response values using regression and ANN model in resistance spot welding of 17-4 precipitation hardened stainless steel. J Adv Manuf Syst, 2022, 21(2): 275-291.

[15.] Tan Y, Fang P, Zhang Y et al (1999) Evaluating nugget sizes of spot welds by using artificial neural network. In: Proceedings of the 6th international conference on computational intelligence, theory and applications: fuzzy days, 25‒28 May, Springer, Berlin

[16.] Panza L, Bruno G, Antal G, et al. Machine learning tool for the prediction of electrode wear effect on the quality of resistance spot welds. Int J Interact Des Manuf, 2024.

[17.] Kitano H, Nakamura T. Combined artificial neural network and least squares method for exploring relationships between welding conditions and weld characteristics. Weld Lett, 2018.

[18.] Kitano H, Nakamura T. Automatic derivation of empirical formulas for characteristics of weld joints using machine learning based technique. J Jpn Weld Soc, 2019, 88(7): 532-535.

[19.] Kitano H. Numeric law discovery and knowledge extraction from welding phenomena using machine learning technique. Mater Jpn, 2019, 58(8): 449-452.

[20.] Kitano H, Sato A, Iyota M, et al. Investigation of relationship between resistance spot welding condition and nugget shape by utilizing machine learning based technique. Weld Int, 2021, 33(4/6): 223-230.

[21.] Gavidel SR, Jeremy L. Performance analysis and comparison of machine learning algorithms for predicting nugget width of resistance spot welding joints. Int J Adv Manuf Technol, 2019, 105: 3779-3796.

[22.] Li M, Liu Z, Huang L, et al. Automatic identification framework of the geometric parameters on self-piercing riveting cross-section using deep learning. J Manuf Process, 2022, 83: 427-437.

[23.] Zhang Z, Wen G, Chen S. Weld image deep learning-based on-line defects detection using convolutional neural networks for Al alloy in robotic arc welding. J Manuf Process, 2019, 45: 208-216.

[24.] Dai W, Li D, Tang D, et al. Deep learning assisted vision inspection of resistance spot welds. J Manuf Process, 2021, 62(8): 262-274.

[25.] Park C, Haftka RT, Kim NH. Remarks on multi-fidelity surrogates. Struct Multidiscip Optim, 2017, 55: 1029-1050.

[26.] Zhang X, Xie F, Ji T, et al. Multi-fidelity deep neural network surrogate model for aerodynamic shape optimization. Comput Method Appl Mech Eng, 2021, 373.

[27.] Zhang Y, Kim NH, Park C, et al. Multi-fidelity surrogate based on single linear regression. AIAA J, 2017, 56(12): 1-9.

[28.] Li M, Liu Z, Huang L, et al. Multi-fidelity data-driven optimization design framework for self-piercing riveting process parameters. J Manuf Process, 2023, 99: 812-824.

Funding

National Natural Science Foundation(52205377); National Key Research and Development Program(2022YFB4601804); Key Basic Research Project of Suzhou(#SJC2022031)