On the application of machine learning techniques for quality assurance in an automobile paint shop

Abstract The automotive industry depends on high-quality paint coatings for both aesthetic appeal and functional performance. However, surface imperfections such as scratches, paint runs, and orange peel can arise from process and environmental variations. This study employs machine learning (ML) and exploratory data analysis (EDA) to identify key factors that influence surface defect formation in automotive painting. Using historical production data from Lucky Motor Corporation Limited, models based on linear regression, support vector machines (SVM), and random forests were developed and validated under various process conditions. The best-performing model achieved an R² of 0.94, with a mean absolute error (MAE) of 0.125 and mean squared error (MSE) of 0.033, demonstrating high predictive accuracy. The proposed ML framework offers a data-driven approach for quality control and process optimization, with the potential to enhance production efficiency, reduce waste, and improve overall paint surface quality. Graphical abstract