Welded Joint Performance Analysis through Mechanical and Thermal Property Prediction with Random Forest

The research conducted in this study utilizes a comprehensive dataset that incorporates various materials, welding procedures, and climatic variables in order to train the Random Forest model. Our objective is to conduct a full evaluation of the performance of welded joints, specifically for their applicability in certain applications, by integrating predictions of both mechanical and thermal properties. The Random Forest model exhibits amazing predictive skills, allowing for accurate estimation of crucial parameters such as tensile strength, impact toughness, and thermal conductivity. This methodology not only optimizes the assessment of welded joint efficacy but also mitigates the necessity for significant and laborious physical experimentation. Our research findings emphasize the significance of a number of factors, such as welding parameters, base material selection, and joint shape, in determining the performance of joints.