Towards an Effective Tool Wear Monitoring System with an AI Model Management Platform
Journal
IEEE 22nd International Conference on Industrial Informatics (INDIN)
Start Page
1
End Page
6
Date Issued
2024-08-18
Author(s)
Abstract
Automated 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.
Event(s)
22nd IEEE International Conference on Industrial Informatics, INDIN 2024, Beijing. 18 August 2024 through 20 August 2024. Code 205093
Subjects
AI Model Management Platform
Sensor Fusion
Tool Wear Prediction System
Publisher
IEEE
Type
conference paper