Thin plates are widely utilized in aircraft, shipbuilding, and automotive industries to meet the requirements of lightweight components. Especially in modern shipbuilding, the thin plate structures not only meet the economic requirements of shipbuilding but also meet the strength and rigidity requirements of the ship. However, a thin plate is less stable and prone to destabilizing deformation in the welding process, which seriously affects the accuracy and performance of the thin plate welding structure. Therefore, it is beneficial to predict welding deformation and residual stress before welding. In this paper, a thin plate welding deformation and residual stress prediction model based on particle swarm optimization (PSO) and grid search(GS) improved support vector regression (PSO-GS-SVR) is established. The welding speed, welding current, welding voltage, and plate thickness are taken as input parameters of the improved support vector regression model, while longitudinal and transverse deformation and residual stress are taken as corresponding outputs. To improve the prediction accuracy of the support vector regression model, particle swarm optimization and grid search are used to optimize the parameters. The results show that the improved support regression model can accurately evaluate the deformation and residual stress of butt welding and has important engineering guiding significance.

中文翻译：

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利用改进的支持向量回归预测薄板的焊接变形和残余应力

薄板广泛用于飞机，造船和汽车行业，以满足轻量化组件的要求。特别是在现代造船中，薄板结构不仅满足造船的经济要求，还满足船舶的强度和刚度要求。然而，薄板的稳定性较差，并且在焊接过程中易于变形，这严重影响了薄板焊接结构的精度和性能。因此，在焊接之前预测焊接变形和残余应力是有益的。建立了基于粒子群算法（PSO）和网格搜索（GS）改进支持向量回归（PSO-GS-SVR）的薄板焊接变形和残余应力预测模型。焊接速度，焊接电流，焊接电压和板厚作为改进的支持向量回归模型的输入参数，而纵向和横向变形以及残余应力则作为相应的输出。为了提高支持向量回归模型的预测精度，使用了粒子群优化和网格搜索来优化参数。结果表明，改进的支持回归模型能够准确评估对接焊缝的变形和残余应力，具有重要的工程指导意义。粒子群优化和网格搜索用于优化参数。结果表明，改进的支持回归模型能够准确评估对接焊缝的变形和残余应力，具有重要的工程指导意义。粒子群优化和网格搜索用于优化参数。结果表明，改进的支持回归模型能够准确评估对接焊缝的变形和残余应力，具有重要的工程指导意义。