The present study deals with the extended version of our previous research work. In this article, for predicting the entire weld bead geometry and engineering stress–strain curve of the cold metal transfer (CMT) weldment, a MATLAB based application window (second version) is developed with certain modifications. In the first version, for predicting the entire weld bead geometry, apart from weld bead characteristics, x and y coordinates (24 from each) of the extracted points are considered. Finally, in the first version, 53 output values (five for weld bead characteristics and 48 for x and y coordinates) are predicted using both multiple regression analysis (MRA) and adaptive neuro fuzzy inference system (ANFIS) technique to get an idea related to the complete weld bead geometry without performing the actual welding process. The obtained weld bead shapes using both the techniques are compared with the experimentally obtained bead shapes. Based on the results obtained from the first version and the knowledge acquired from literature, the complete shape of weld bead obtained using ANFIS is in good agreement with the experimentally obtained weld bead shape. This motivated us to adopt a hybrid technique known as ANFIS (combined artificial neural network and fuzzy features) alone in this paper for predicting the weld bead shape and engineering stress–strain curve of the welded joint. In the present study, an attempt is made to evaluate the accuracy of the prediction when the number of trials is reduced to half and increasing the number of data points from the macrograph to twice. Complete weld bead geometry and the engineering stress–strain curves were predicted against the input welding parameters (welding current and welding speed), fed by the user in the MATLAB application window. Finally, the entire weld bead geometries were predicted by both the first and the second version are compared and validated with the experimentally obtained weld bead shapes. The similar procedure was followed for predicting the engineering stress–strain curve to compare with experimental outcomes.

本研究涉及我们先前研究工作的扩展版本。在本文中，为了预测冷金属过渡 (CMT) 焊件的整个焊道几何形状和工程应力应变曲线，开发了一个基于 MATLAB 的应用程序窗口（第二版），并进行了一些修改。在第一个版本中，为了预测整个焊道几何形状，除了焊道特征外，还考虑了提取点的x和y坐标（每个点 24 个）。最后，在第一个版本中，53 个输出值（5 个用于焊道特性，48 个用于x和y坐标）使用多元回归分析 (MRA) 和自适应神经模糊推理系统 (ANFIS) 技术进行预测，以在不执行实际焊接过程的情况下获得与完整焊道几何形状相关的想法。使用这两种技术获得的焊道形状与实验获得的焊道形状进行比较。基于从第一个版本获得的结果和从文献中获得的知识，使用 ANFIS 获得的焊道的完整形状与实验获得的焊道形状非常吻合。这促使我们在本文中单独采用称为 ANFIS（组合人工神经网络和模糊特征）的混合技术来预测焊接接头的焊道形状和工程应力应变曲线。在目前的研究中，当试验次数减少到一半并将宏观图中的数据点数增加到两倍时，尝试评估预测的准确性。完整的焊道几何形状和工程应力-应变曲线是根据用户在 MATLAB 应用程序窗口中提供的输入焊接参数（焊接电流和焊接速度）进行预测的。最后，通过第一个和第二个版本预测的整个焊道几何形状与实验获得的焊道形状进行比较和验证。遵循类似的程序来预测工程应力 - 应变曲线以与实验结果进行比较。完整的焊道几何形状和工程应力-应变曲线是根据用户在 MATLAB 应用程序窗口中提供的输入焊接参数（焊接电流和焊接速度）进行预测的。最后，通过第一个和第二个版本预测的整个焊道几何形状与实验获得的焊道形状进行比较和验证。遵循类似的程序来预测工程应力 - 应变曲线以与实验结果进行比较。完整的焊道几何形状和工程应力-应变曲线是根据用户在 MATLAB 应用程序窗口中提供的输入焊接参数（焊接电流和焊接速度）进行预测的。最后，通过第一个和第二个版本预测的整个焊道几何形状与实验获得的焊道形状进行比较和验证。遵循类似的程序来预测工程应力 - 应变曲线以与实验结果进行比较。