This paper aims to propose fuzzy-regression-particle swarm optimization (PSO) based hybrid optimization approach for getting maximum weld quality in terms of weld strength and bead depth of penetration.,The prediction of welding quality to achieve best of it is not possible by any single optimization technique. Therefore, fuzzy technique has been applied to predict the weld quality in terms of weld strength and weld bead geometry in combination with a multi-performance characteristic index (MPCI). Then regression analysis has been applied to develop relation between the MPCI output value and the input welding process parameters. Finally, PSO method has been used to get the optimal welding condition by maximizing the MPCI value.,The predicted weld quality or the MPCI values in terms of combined weld strength and bead geometry has been found to be highly co-related with the weld process parameters. Therefore, it makes the process easy for setting of weld process parameters for achieving best weld quality, as there is no need to finding the relation for individual weld quality parameter and weld process parameters although they are co-related in a complicated manner.,In this paper, a new hybrid approach for predicting the weld quality in terms of both mechanical properties and weld geometry and optimizing the same has been proposed. As these parameters are highly correlated and dependent on the weld process parameters the proposed approach can effectively analyzing the ambiguity and significance of each process and performance parameter.

中文翻译：

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一种基于模糊回归粒子群算法的机器人气体保护金属电弧焊焊接条件选择混合方法

本文旨在提出基于模糊回归粒子群优化 (PSO) 的混合优化方法，以在焊接强度和焊道熔深方面获得最大焊接质量。任何单一的优化技术。因此，模糊技术已被应用于结合多性能特征指数 (MPCI) 来预测焊接强度和焊道几何形状方面的焊接质量。然后应用回归分析来开发 MPCI 输出值与输入焊接工艺参数之间的关系。最后，使用 PSO 方法通过最大化 MPCI 值来获得最佳焊接条件。已经发现，结合焊接强度和焊道几何形状的预测焊接质量或 MPCI 值与焊接工艺参数高度相关。因此，它使设置焊接工艺参数以实现最佳焊接质量的过程变得容易，因为尽管它们以复杂的方式相互关联，但无需找到单个焊接质量参数和焊接工艺参数的关系。,本文提出了一种新的混合方法，用于在机械性能和焊接几何形状方面预测焊接质量并对其进行优化。由于这些参数高度相关并且依赖于焊接工艺参数，因此所提出的方法可以有效地分析每个工艺和性能参数的模糊性和重要性。