Calibration-based tool condition monitoring for repetitive machining operations

Abstract Reliable tool condition monitoring (TCM) system is essential for any machining process in mass production to control the part quality as well as reduce the machine tool downtime and maintenance costs. However, while various research studies have proposed their TCM systems, the complexity in setups with advanced decision-making algorithms and specificity in application to limited cutting conditions continue to complicate the implementation of these systems into practical scenarios. This study develops a very simple and flexible TCM system for repetitive machining operations. The proposed monitoring approach reduces the complexity of monitoring model by considering the important characteristic of repeatability in process which has been commonly found in the mass production scenario and implements the calibration procedure to improve the flexibility of the model application to actual machining processes with complex toolpath designs and variable cutting conditions. The selected cutting tools with specific tool conditions are used in the calibration phase to generate reference signals. In actual repetitive production, the collected signal generated by the cutting tool in each operation is compared with reference signals to identify the most similar condition of the reference tool through the proposed similarity analysis. To validate the performance, the current study demonstrates the application of proposed monitoring approach to monitor the tool wear in repetitive milling operations with complex toolpath, and the predicted tool wear progression is found to be in good agreement with experimental measurements during the machining of multiple parts over the entire tool life.

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