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.
If fatigue failure occurs during aircraft operation, it can cause catastrophic injuries. So, it is necessary to study fatigue failure at the design stage. Frequency domain fatigue analysis is used to predict fatigue failure. During frequency domain fatigue analysis, results can be calibrated by PSD analysis. In this study, fatigue failure is predicted by the Dirlik method, Lalanne method and Steinberg method. Regarding results, life determined by the Dirlik method, Lalanne method and Steinberg method were 8.737, 8.314, and 7.901 times the standard life, respectively. The Steinberg method is the most conservative but the difference with other methods was approximately 10%. In the cycle histogram, the Dirlik method had more counts than the Lalanne method in lower stress range. However, it does not affect the life of material used in this study. However, if material has a lower fatigue limit or stronger PSD data is used, life difference will occur.
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