Application of Machine Learning Models to Optimize the Quality of Produced Parts in the Automotive Industry

Abstract

Integrating machine learning techniques into automotive quality control improves responsiveness, accuracy, and efficiency, thereby reducing costs and increasing customer satisfaction. The study focuses on the cutting process of a mechanical transmission shaft manufactured in the Moroccan automotive industry. Two models, Decision Tree and Random Forest, are used to analyze the impact of parameters on the conformity of the parts, including length and diameter parameters. The results show that diameter is the key factor influencing quality, and the Random Forest model, with more training data, demonstrates superior performance, achieving perfect precision and recall. The analysis helps identify critical parameters, especially diameter, to optimize the manufacturing process by reducing the rate of non-conforming parts.