Prediction of whether it is in the ideal norm range of welding arguments that used in applications of resistance spot welding in automotive industry by applying machine learning

Spot welding, a type of resistance welding, is a welding application widely used in the production area and it is a common method for joining metal sheets. The spot-welding process is widely used in many production areas, especially in the automotive industry, radiator, and wire mesh production. Spot welding in car production lines is mainly performed by robotic applications. Industry 4.0 and digital transformation trends have led to unprecedented data growth. Nowadays, the manufacturing industry benefits from the power of machine learning and data science algorithms to monitor production processes and make predictions for quality, maintenance, and production optimization. Applying machine learning algorithms reduces the duration and cost of experiments. This study aims to confirm whether spot welding, applied by robotic arms, is within the ideal spot-welding norms, in real production areas. The study was conducted at the TOFAŞ factory to utilize real production data, specifically focusing on the body production assembly line as a pilot line. The research utilized a dataset consisting of current welding parameters in 2023. The experiments were conducted on a dataset, and the performance of various machine learning algorithms was evaluated to determine the most suitable prediction method. Considering the scores of the Area Under the ROC Curve (AUC), XGBoost, LightGBM, and CatBoost models exhibited the highest performance with a success rate of 99%.