Jian Wen ChenMENG-SHIUN TSAIChe Lun Hung2025-05-222025-05-222024-08-18https://www.scopus.com/record/display.uri?eid=2-s2.0-85215525325&origin=resultslisthttps://scholars.lib.ntu.edu.tw/handle/123456789/729668Automated monitoring of tool wear is crucial for maintaining product quality. Furthermore, implementing AI techniques for real-time tool monitoring involves not only developing models but also managing their versions, avoiding the issue of models becoming less accurate as the properties of the machinery change over time. Consequently, this study develops a tool wear prediction system integrated with an artificial intelligent (AI) model management platform. First, this system uses various machine learning models to extract diverse signal features from sensor fusion, thereby boosting the accuracy of tool wear prediction. Secondly, the AI Models Management Platform comprises the C# programming language, Neural Networks Processing Unit (NPU) board, and Docker on both user and server sides, enhancing industrial processes and enabling real-time analysis of sensor data. According to these results, the Ensemble Learning method within the machine learning model demonstrates superior performance, yielding an average root mean squared error (RMSE) of 0.000185 mm2. Additionally, AI model management platform efficiently handle various model versions and streamline data training processes, empowering users to select suitable models and thereby enhancing system robustness.AI Model Management PlatformSensor FusionTool Wear Prediction Systemconference paper10.1109/INDIN58382.2024.10774399