摘要
以MAG焊焊接电压、焊接速度、送丝速度为可调工艺参数,开展了三因素三水平全因子平板对接焊和堆焊试验.基于试验数据建立了误差反向传播神经网络、径向基神经网络和克里金模型来预测焊缝余高、接头抗拉强度和冲击吸收能量.模型预测结果显示,所建立模型均能较好的预测焊缝性能,但是没有一个模型能同时最佳预测三种焊缝性能且各模型预测波动较大.为了进一步提升预测精度和稳定性,将误差反向传播神经网络、径向基神经网络和克里金模型以线性加权法组合.结果表明,组合模型能提升预测的精度和稳定性.基于组合模型,采用NSGA-II算法实现多目标优化,得到并验证了焊缝余高、接头冲击吸收能量和抗拉强度三者间的非劣解.验证结果表明焊接工艺多目标优化对实现焊缝综合性能整体最优以及焊接精细化应用具有较大的指导意义.
Three-factor and three-level full factor beadon plate and butting welding tests were carried out with welding voltage,welding speed and wire feed rate being taken as considerable process parameters.Based on the data of the tests,error back propagation neural network,radial basis function neural network and kriging model were established to predict weld reinforcement,the tensile strength and impact energy of the weld generated in the tests.The results of model prediction show that the models can predict the weld performance well,but there are no models that can predict the three kinds of weld performance at the same time,and the prediction of each model fluctuates greatly.In order to further improve the precision and stability of prediction,the three stand-alone models mentioned above were combined into ensemble models in the manner of linear weighting method.Then,the multi-objective optimization of process parameters was achieved by NSGA-II based on the ensemble models.Finally,the non-inferior solutions between the weld reinforcement,the tensile strength and impact energy of the joints are obtained and verified.It is of great significance to realize the overall optimization of weld comprehensive performance and the fine application of welding.
作者
吕小青
王旭
徐连勇
荆洪阳
韩永典
LV Xiaoqing;WANG Xu;XU Lianyong;JING Hongyang;HAN Yongdian(Tianjin University,Tianjin,300072,China;Tianjin Key Laboratory of Advanced Joining Technology,Tianjin,300072,China)
出处
《焊接学报》
EI
CAS
CSCD
北大核心
2020年第2期6-11,I0005,共7页
Transactions of The China Welding Institution
基金
国家重点研发计划资助(2017YFB1303300)。
关键词
多目标优化
焊接工艺
组合模型
神经网络
克里金模型
multi-objective optimization
welding process
ensemble models
neural network
Kriging
