Heat distribution in the submerged arc welding (SAW) process has a significant impact on the quality of welds. In this paper, a machine learning method is proposed to predict and analyze temperature in the SAW process. Thermal video data is obtained from an infrared camera at the bottom surface of the workpiece. Programs written in MATLAB are used to extract the temperature history and to generate transient isotherm using image processing techniques. The evolution of the transient isotherm throughout the heating and cooling cycle is analyzed quantitatively. The experimental datasets are suitably prepared for use in machine learning. Prediction of local temperature, temperature-time curve, and temperature field map is made through the proposed machine learning method. Various additional characteristics of the temperature field map are analyzed and predicted. The proposed method is experimentally validated and the predicted results agree closely with the experimental data.

埋弧焊（SAW）工艺中的热量分布对焊缝质量有重大影响。本文提出了一种机器学习方法来预测和分析声表面波过程中的温度。热视频数据是从位于工件底表面的红外摄像机获得的。用MATLAB编写的程序用于提取温度历史记录，并使用图像处理技术生成瞬时等温线。定量分析了整个加热和冷却周期中瞬态等温线的演变。实验数据集已适当准备用于机器学习。通过提出的机器学习方法对局部温度，温度-时间曲线和温度场图进行了预测。分析并预测了温度场图的各种其他特征。该方法经过实验验证，预测结果与实验数据吻合良好。