Milling process monitoring based on intelligent real-time parameter identification for unmanned manufacturing
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Date
2024
Journal Title
Journal ISSN
Volume Title
Publisher
Elsevier Inc.
Abstract
This study addresses the critical need for intelligent process monitoring in unmanned manufacturing through real-time fault detection. The proposed hybrid approach, which is focused on overcoming the limitations of existing methods, utilizes machine learning (ML) for precise parameter identification in real-time to detect deviations. The ML system is developed using extensive data obtained from simulations based on enhanced force models also achieved through ML. Demonstrating over 96 % accuracy in real-time predictions, the method proves applicable for diverse unmanned manufacturing applications, including monitoring and process optimization, emphasizing its adaptability for industrial implementation using CNC controller signals. © 2024 CIRP
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Keywords
Machine learning, Milling, Monitoring
Turkish CoHE Thesis Center URL
Citation
0
WoS Q
Q2
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Q1
Source
CIRP Annals