, 이원웅2, 이상탁3, 장윤4, 이재곤2, 이명교1
, Won Woong Lee2, Sang Tak Lee3, Yoon Jang4, Jae Gon Lee2, Myoung Gyo Lee1
The automotive painting process is complex, featuring hybrid serial-parallel lines and unplanned repair operations, which makes production forecasting challenging. This study introduces an AI-driven predictive framework designed to estimate future work-in-process (WIP) in paint shops, with the goal of improving production management efficiency. We collected and preprocessed historical operational data through noise reduction and process filtering. Several machine learning and deep learning models were trained and validated. To ensure transparency, we utilized explainable AI (XAI) techniques. The proposed system proved feasible for deployment on a web-based monitoring platform, facilitating real-time decision-making in manufacturing environments.
