Citation: | ZHUO Wenbo, TAN Guobi, CHEN Qiuren, HOU Zehong, WANG Xianhui, HAN Weijian, HUANG Li. Multi-objective optimization of resistance spot welding process parameters of ultra-high strength steel based on agent model and NSGA-Ⅱ[J]. TRANSACTIONS OF THE CHINA WELDING INSTITUTION, 2024, 45(4): 20-25. DOI: 10.12073/j.hjxb.20230317002 |
In order to find the best welding process parameters for resistance spot welding of ultra-high strength steel, a three-factor and five-level flat plate lap spot welding experiment designed by orthogonal test method was carried out. With welding time, welding current and electrode pressure as adjustable process parameters, the nugget diameter, indentation depth, the tension-shear strength and spatter were used as the quality evaluation indicators of welded joints. Based on Gaussian process regression and BP neural network, a proxy model of the relationship between the process parameters and the quality evaluation indicators of welded joints was established. The training results showed that the accuracy of the model was very high. Finally, the multi-objective optimization was realized by using the genetic algorithm NSGA-Ⅱ with elite strategy and non-dominated sequencing, and the optimal pareto solution set between the evaluation indicators was obtained. The relative error value of each evaluation model was very small, which indicated that the optimization method had good prediction effect and stability. By using less experimental data, the method of establishing the optimization model had important guiding value for the selection of the best welding process parameters in resistance spot welding and other welding fields.
[1] |
Volkers S, Somonov V, Bohm S, et al. Influence on the microstructure of laser beam welds of high-strength steels[J]. Lightweight Design Worldwide, 2017, 10(4): 40 − 45.
|
[2] |
高丽, 周月明, 刘俊亮, 等. 双相钢的研究进展及应用[C]//第七届(2009)中国钢铁年会大会论文集(中), 2009: 938-942.
Gao Li, Zhou Yueming, Liu Junliang, et al. Research progress and application of dual-phase steel [C]//Proceedings of the 7th (2009) China Steel Annual Conference (middle), 2009: 938-942.
|
[3] |
Shi G, Westgate S A. Resistance spot welding of high strength steels[J]. International Journal for the Joining of Materials, 2004, 16(1): 9 − 14.
|
[4] |
Eusebio J M, Jose A E, Valentin M, et al. Optimization of geometric parameters in a welded joint through response surface methodology[J]. Construction and Building Materials, 2017, 154: 105 − 114. doi: 10.1016/j.conbuildmat.2017.07.163
|
[5] |
Yang Y, Cao L, Wang C, et al. Multi-objective process parameters optimization of hot-wire laser welding using ensemble of metamodels and NSGA-II[J]. Robotics and Computer-Integrated Manufacturing, 2018, 53: 141 − 152. doi: 10.1016/j.rcim.2018.03.007
|
[6] |
陶永杰. 面向低碳制造的铝合金薄板激光搅拌焊接工艺参数优化[D]. 武汉: 华中科技大学, 2021.
Tao Yongjie. Optimization of laser stir welding process parameters of aluminum alloy sheet for low-carbon manufacturing [D]. Wuhan: Huazhong University of Science and Technology, 2021.
|
[7] |
吕小青, 王旭, 徐连勇, 等. 基于组合模型的MAG焊工艺参数多目标优化[J]. 焊接学报, 2020, 41(2): 6 − 11. doi: 10.12073/j.hjxb.20190629001
Lü Xiaoqing, Wang Xu, Xu Lianyong, et al. Multi-objective optimization of MAG welding process parameters based on combination model[J]. Transactions of the China Welding Institution, 2020, 41(2): 6 − 11. doi: 10.12073/j.hjxb.20190629001
|
[8] |
Djuric A, Mili D, Klobar D, et al. Multi-objective optimization of the resistance spot-welding process parameters for the welding of dual-phase steel DP500[J]. Materials and Technologies, 2021, 55(2): 201 − 206.
|
[9] |
Zhao D, Ivanov M, Wang Y, et al. Multi-objective optimization of the resistance spot welding process using a hybrid approach[J]. Journal of Intelligent Manufacturing, 2020, 32: 2219 − 2234.
|
[10] |
Zhang G, Lin T, Luo L, et al. Multi-objective optimization of resistance welding process of GF/PP composites.[J]. Multidisciplinary Digital Publishing Institute, 2021, 13(15): 2560.
|
[11] |
姚煜, 胡涛, 付建勋, 等. 小样本分散数据的回归建模和多目标优化[J]. 上海大学学报(自然科学版), 2022, 28(3): 451 − 462.
Yao Yu, Hu Tao, Fu Jianxun, et al. Regression modeling and multi-objective optimization of small sample scattered data[J]. Journal of Shanghai University (Natural Science Edition), 2022, 28(3): 451 − 462.
|
[12] |
许方敏, 许忠斌, 朱科, 等. 基于高斯过程回归的注塑质量多目标优化方法[J]. 塑料工业, 2022, 50(4): 94 − 98,122. doi: 10.3969/j.issn.1005-5770.2022.04.015
Xu Fangmin, Xu Zhongbin, Zhu Ke, et al. Multi-objective optimization method for injection quality based on Gaussian process regression[J]. Plastic Industry, 2022, 50(4): 94 − 98,122. doi: 10.3969/j.issn.1005-5770.2022.04.015
|
[13] |
Palanivel R, Dinaharan I, Laubscher R F. Application of an artificial neural network model to predict the ultimate tensile strength of friction welded titanium tubes[J]. Journal of the Brazilian Society of Mechanical Sciences and Engineering, 2019, 41: 1 − 13. doi: 10.1007/s40430-018-1505-x
|
[14] |
杨亚超, 全惠敏, 邓林峰, 等. 基于神经网络的焊机参数预测方法[J]. 焊接学报, 2018, 39(1): 32 − 36. doi: 10.12073/j.hjxb.2018390008
Yang Yachao, Quan Huimin, Deng Linfeng, et al. Prediction method of welding machine parameters based on neural network[J]. Transactions of the China Welding Institution, 2018, 39(1): 32 − 36. doi: 10.12073/j.hjxb.2018390008
|
[15] |
刘艺繁, 阎春平, 倪恒欣, 等. 基于GABP和改进NSGA-Ⅱ的高速干切滚齿工艺参数多目标优化决策[J]. 中国机械工程, 2021, 32(9): 1043 − 1050. doi: 10.3969/j.issn.1004-132X.2021.09.005
Liu Yifan, Yan Chunping, Ni Hengxin, et al. Multi-objective optimization decision of high-speed dry cutting gear hobbing process parameters based on GABP and improved NSGA-Ⅱ[J]. China Mechanical Engineering, 2021, 32(9): 1043 − 1050. doi: 10.3969/j.issn.1004-132X.2021.09.005
|
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