基于代理模型和NSGA-Ⅱ的超高强钢电阻点焊工艺参数多目标优化

Multi-objective optimization of resistance spot welding process parameters of ultra-high strength steel based on agent model and NSGA-Ⅱ

为寻求超高强钢电阻点焊时最佳的焊接工艺参数,开展正交试验法设计三因素五水平的平板搭接点焊试验,以焊接时间、焊接电流和电极压力为可调的工艺参数,将熔核直径、压痕深度、拉剪强度及飞溅情况作为焊接接头质量评价指标.基于高斯过程回归和BP神经网络建立起焊接工艺参数与焊接接头质量评价指标之间关系的代理模型,训练的结果显示模型精度很高.最后利用带精英策略的非支配排序的遗传算法NSGA-Ⅱ实现多目标优化,得到各评价指标之间的最优pareto解集.经验证,各评价模型的相对误差值都很小.结果表明,该优化方法有较好的预测效果和稳定性.通过使用较少的试验数据,建立优化模型的方法对电阻点焊及其它焊接领域最佳焊接工艺参数的选取具有重要的指导价值.

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-Ⅱwith 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.