广西科技大学学报2026，Vol.37Issue(2)：25-34,10.DOI:10.16375/j.cnki.cn45-1395/t.2026.02.004
基于NSGAⅡ算法和BP神经网络的轮辋焊接工艺参数优化
Wheel rim welding process parameters based on NSGAⅡ algorithm and BP neural network
摘要
Abstract
The propensity for cracking at the weld seam during the flaring process after flash butt welding of rims was investigated from two perspectives:the metallographic microstructures of the base metal,weld zone,and heat-affected zone,and the fracture morphology.Based on the analysis,recommendations for optimizing welding process parameters were proposed.To improve the quality of flash butt welded joints of Q355D rim steel,an experimental study was conducted.A BP-NSGAⅡ algorithm was employed to optimize four input process parameters,namely burn-off length,burn-off speed,upset distance,and energized upset time,while elongation and impact toughness were selected as output indicators.The results indicate that the trained BP neural network satisfies the required prediction accuracy.By integrating it with the NSGAⅡ algorithm and performing multiple iterations,an optimal combination of process parameters was obtained.The optimized parameters were determined as follows:a burn-off length of 18 mm,a burn-off speed of 1.1 mm/s,an upset distance of 10.0 mm,and an energized upset time of 0.35 s.Validation through tensile and impact tests showed that the measured elongation reached 25.10%,and the impact absorbed energy was 22.6 J.Application of these optimized welding parameters in production and subsequent tracking demonstrated that the actual cracking rate of rims was effectively controlled below 15%.关键词
轮辋开裂分析/NSGAⅡ算法/BP神经网络/闪光对焊/焊接工艺/参数优化Key words
analysis of wheel rim cracking/NSGAⅡ algorithm/BP neural network/flash butt welding/welding process/parameter optimization分类
矿业与冶金引用本文复制引用
何世明,石光林,刘文选,赵帅,彭蔚杰,王文枫,玉文雁..基于NSGAⅡ算法和BP神经网络的轮辋焊接工艺参数优化[J].广西科技大学学报,2026,37(2):25-34,10.基金项目
中央引导地方科技发展资金项目(ZY19183003)资助 （ZY19183003）
