The present work describes a computational framework based on finite element (FE) analysis and machine learning (ML) and genetic algorithm (GA) to accurately estimate and minimize the residual stresses in welding. The FE analysis comprises of sequentially coupled thermal and mechanical analyses that are nonlinear due to temperature-dependent thermal/mechanical properties. In the FE analysis, the effective stress function (ESF) algorithm is used for accurate computation of the temperature-induced elasto-plastic stress field during welding. The ESF algorithm accounts for the large variation of yield curves due to temperature changes in welding. To improve the accuracy of the thermal and mechanical analyses, an optimization process combining supervised machine learning and binary coded genetic algorithm is utilized. This optimization technique is implemented to derive an accurate phase-change model for the thermal FE analysis of SS304, which is otherwise a burdensome and costly task to obtain from calorimetric experiments. The results obtained through the derived phase-change model are validated through welding time-temperature distribution reported for a welding experiment in literature. The same optimization process is further used to obtain a set of weld operating conditions (such as weld voltage, current, welding speed, and the gap between plates) for SS304 that reduce the tensile residual stresses. It is found that the accurate estimation and reduction of welding residual stresses can be realized by using the present computational framework.

目前的工作描述了一个基于有限元 (FE) 分析和机器学习 (ML) 和遗传算法 (GA) 的计算框架，以准确估计和最小化焊接中的残余应力。有限元分析包括顺序耦合的热和机械分析，由于温度相关的热/机械特性，这些分析是非线性的。在有限元分析中，有效应力函数（ESF）算法用于精确计算焊接过程中温度引起的弹塑性应力场。ESF 算法解释了由于焊接温度变化导致的屈服曲线的大变化。为了提高热分析和机械分析的准确性，使用了结合监督机器学习和二进制编码遗传算法的优化过程。实施这种优化技术是为了推导出一个准确的相变模型，用于 SS304 的热有限元分析，否则从量热实验中获得该模型是一项繁重且成本高昂的任务。通过导出的相变模型获得的结果通过文献中报道的焊接实验的焊接时间-温度分布得到验证。相同的优化过程进一步用于获得 SS304 降低拉伸残余应力的一组焊接操作条件（如焊接电压、电流、焊接速度和板间间隙）。发现使用本计算框架可以实现对焊接残余应力的准确估计和降低。否则，这是从量热实验中获得的一项繁重且昂贵的任务。通过导出的相变模型获得的结果通过文献中报道的焊接实验的焊接时间-温度分布得到验证。相同的优化过程进一步用于获得 SS304 降低拉伸残余应力的一组焊接操作条件（如焊接电压、电流、焊接速度和板间间隙）。发现使用本计算框架可以实现对焊接残余应力的准确估计和降低。否则，这是从量热实验中获得的一项繁重且昂贵的任务。通过导出的相变模型获得的结果通过文献中报道的焊接实验的焊接时间-温度分布得到验证。相同的优化过程进一步用于获得 SS304 降低拉伸残余应力的一组焊接操作条件（如焊接电压、电流、焊接速度和板间间隙）。发现使用本计算框架可以实现对焊接残余应力的准确估计和降低。相同的优化过程进一步用于获得 SS304 降低拉伸残余应力的一组焊接操作条件（如焊接电压、电流、焊接速度和板间间隙）。发现使用本计算框架可以实现对焊接残余应力的准确估计和降低。相同的优化过程进一步用于获得 SS304 降低拉伸残余应力的一组焊接操作条件（如焊接电压、电流、焊接速度和板间间隙）。发现使用本计算框架可以实现对焊接残余应力的准确估计和降低。