Li Ran, Dong Manshu, Gao Hongming
State Key Laboratory of Advanced Welding and Joining, Harbin Institute of Technology, West Straight Street 92, Harbin 150001, China.
Ningxia Tiandi Benniu Industrial Group Co., Ltd., Shizuishan 753000, China.
Materials (Basel). 2021 Mar 18;14(6):1494. doi: 10.3390/ma14061494.
Bead size and shape are important considerations for industry design and quality detection. It is hard to deduce an appropriate mathematical model for predicting the bead geometry in a continually changing welding process due to the complex interrelationship between different welding parameters and the actual bead. In this paper, an artificial neural network model for predicting the bead geometry with changing welding speed was developed. The experiment was performed by a welding robot in gas metal arc welding process. The welding speed was stochastically changed during the welding process. By transient response tests, it was indicated that the changing welding speed had a spatial influence on bead geometry, which ranged from 10 mm backward to 22 mm forward with certain welding parameters. For this study, the input parameters of model were the spatial welding speed sequence, and the output parameters were bead width and reinforcement. The bead geometry was recognized by polynomial fitting of the profile coordinates, as measured by a structured laser light sensor. The results showed that the model with the structure of 33-6-2 had achieved high accuracy in both the training dataset and test dataset, which were 99% and 96%, respectively.
焊珠尺寸和形状是工业设计和质量检测的重要考虑因素。由于不同焊接参数与实际焊珠之间存在复杂的相互关系，在不断变化的焊接过程中，很难推导出一个合适的数学模型来预测焊珠几何形状。本文建立了一个用于预测随焊接速度变化的焊珠几何形状的人工神经网络模型。实验由焊接机器人在气体保护金属极电弧焊过程中进行。焊接过程中焊接速度随机变化。通过瞬态响应测试表明，在一定焊接参数下，变化的焊接速度对焊珠几何形状有空间影响，范围从向后10毫米到向前22毫米。对于本研究，模型的输入参数是空间焊接速度序列，输出参数是焊珠宽度和余高。通过结构激光光传感器测量的轮廓坐标的多项式拟合来识别焊珠几何形状。结果表明，结构为33-6-2的模型在训练数据集和测试数据集中均达到了高精度，分别为99%和96%。
