학술논문
Sensor Data Modeling and Model Frequency Analysis for Detecting Cutting Tool Anomalies in Machining
Document Type
Periodical
Author
Source
IEEE Transactions on Systems, Man, and Cybernetics: Systems IEEE Trans. Syst. Man Cybern, Syst. Systems, Man, and Cybernetics: Systems, IEEE Transactions on. 53(5):2641-2653 May, 2023
Subject
Language
ISSN
2168-2216
2168-2232
2168-2232
Abstract
Tool condition monitoring (TCM) in advanced manufacturing is concerned with cutting tool operational status monitoring and damage diagnosis. In the present study, an innovative TCM approach based on sensor data modeling and model frequency analysis is proposed. The new approach creates a paradigmatic shift to the conventional TCM techniques and can potentially realize autonomous cutting tool anomaly diagnosis satisfying the requirement of advanced manufacturing. When applying the proposed approach, the data from sensors are not directly utilized for monitoring cutting tool status. Instead, the data from sensors are utilized to build a dynamic process model. This allows the unique frequency-domain properties of the machining process to be extracted and used to reveal, in real time, cutting tool health conditions. Experimental studies are conducted to verify the effectiveness of the proposed approach and to demonstrate the superiority of the new approach over conventional TCM techniques.