Machine learning method to predict and analyse transient temperature in submerged arc welding

Abstract

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.