In this paper, an effort is made to determine the optimized parameters in laser welding of Hastelloy C-276 using Artificial Neural Network (ANN) and Genetic Algorithm (GA). CO2 Laser welding was performed on a sheet of thickness 1.6[Formula: see text]mm based on Taguchi L27 orthogonal array. Laser power, welding speed and shielding gas flow rate were chosen as input parameters and Bead width, depth of Penetration and Microhardness were measured for assessing the weld quality. ANN was applied for modeling the welding process parameters i.e. heat input, welding speed and gas flow rate. Various learning algorithms such as Batch Back Propagation (BBP), Incremental Back Propagation (IBP), Quick Propagation (QP) and Levenberg–Marquardt (LM) were comprehensively tested for estimating the output parameters and a comparison was also made among them, with respect to prediction accuracy. BBP method was found to be the best learning algorithm. Experimental validation test was performed based on the ANN and GA predicted optimized responses and this welding input parameters provided satisfactory weld metal characteristics in terms of penetration depth, bead width and microhardness.

中文翻译：

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利用人工神经网络和遗传算法优化哈氏合金C-276激光焊接工艺参数

在本文中，努力使用人工神经网络 (ANN) 和遗传算法 (GA) 确定 Hastelloy C-276 激光焊接的优化参数。一氧化碳2基于Taguchi L27正交阵列在厚度为1.6[公式：见正文]mm的片材上进行激光焊接。选择激光功率、焊接速度和保护气体流量作为输入参数，测量焊道宽度、穿透深度和显微硬度以评估焊接质量。人工神经网络用于模拟焊接工艺参数，即热输入、焊接速度和气体流量。综合测试了批反向传播（BBP）、增量反向传播（IBP）、快速传播（QP）和Levenberg-Marquardt（LM）等各种学习算法来估计输出参数，并在它们之间进行了比较，关于到预测准确度。BBP方法被认为是最好的学习算法。