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An edge-based algorithm for tool wear monitoring in repetitive milling processes

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Abstract

In the era of Industry 4.0, cloud computing has attracted a lot of attention from industrial organizations in realizing smart manufacturing. However, considering the latency issue of cloud computing, the time-sensitive data are more suitable to be processed through edge computing close to the data source, which has been recognized as a potential solution to enable the real-time monitoring in the machining industry, especially for the small and medium-sized manufacturers. Due to the limitations of available data and computing capability at the edge location, it is still very challenging to realize edge computing for complex machining monitoring. To satisfy this research need, a calibration-based tool condition monitoring (TCM) is developed to monitor the tool wear progression in repetitive machining processes by comparing the characteristic signals generated by the reference cutting tools in the calibration procedure with the signal generated by the cutting tool being monitored through a concise similarity analysis. The proposed algorithm can be easily integrated into typical cyber-psychical systems to realize the edge computing in a very efficient and flexible way. To validate the performance of the proposed algorithm, a case study is demonstrated for tool wear monitoring of repetitive milling processes. Experimental validation has shown that the proposed calibration-based TCM algorithm can effectively realize the edge computing in tool wear monitoring through a simple calculation.

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  1. Rochester Institute of Technology, 1 Lomb Memorial Drive, 14623, Rochester, NY, United States

    Rui Liu

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Liu, R. An edge-based algorithm for tool wear monitoring in repetitive milling processes. J Intell Manuf 34, 2333–2343 (2023). https://doi.org/10.1007/s10845-022-01925-0

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