Document Type
Article
Publication Date
5-19-2026
Department
Department of Manufacturing and Mechanical Engineering Technology
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.
Publication Title
International Journal of System Assurance Engineering and Management
Recommended Citation
Fatima, A.,
Ahmed, S.,
&
Shan, M.
(2026).
On the application of machine learning techniques for quality assurance in an automobile paint shop.
International Journal of System Assurance Engineering and Management.
http://doi.org/10.1007/s13198-026-03344-3
Retrieved from: https://digitalcommons.mtu.edu/michigantech-p2/2669
Creative Commons License
This work is licensed under a Creative Commons Attribution 4.0 International License.
Version
Publisher's PDF
Publisher's Statement
© The Author(s) 2026. Publisher’s version of record: https://doi.org/10.1007/s13198-026-03344-3