Tool Condition Monitoring for High-Performance Machining Systems—A Review

Sensors

Mahmoud Hassan¹  Ayman Mohamed²  Helmi Attia²  Rachid M’Saoubi³ 

[1] Advanced Material Removal Processes, Aerospace Manufacturing Technologies Center (AMTC), National Research Council Canada, Ottawa, ON K1A 0R6, Canada;Department of Mechanical Engineering, McGill University, Montreal, QC H3A 0C3, Canada;R&D Material and Technology Development, Seco Tools AB, SE-73782 Fagersta, Sweden;

关键词: tool condition monitoring;    machine learning;    sensor fusion;    milling process;    signal processing;    feature extraction;   

DOI  :  10.3390/s22062206

来源: DOAJ

【 摘 要 】

In the era of the “Industry 4.0” revolution, self-adjusting and unmanned machining systems have gained considerable interest in high-value manufacturing industries to cope with the growing demand for high productivity, standardized part quality, and reduced cost. Tool condition monitoring (TCM) systems pave the way for automated machining through monitoring the state of the cutting tool, including the occurrences of wear, cracks, chipping, and breakage, with the aim of improving the efficiency and economics of the machining process. This article reviews the state-of-the-art TCM system components, namely, means of sensing, data acquisition, signal conditioning and processing, and monitoring models, found in the recent open literature. Special attention is given to analyzing the advantages and limitations of current practices in developing wireless tool-embedded sensor nodes, which enable seamless implementation and Industrial Internet of Things (IIOT) readiness of TCM systems. Additionally, a comprehensive review of the selection of dimensionality reduction techniques is provided due to the lack of clear recommendations and shortcomings of various techniques developed in the literature. Recent attempts for TCM systems’ generalization and enhancement are discussed, along with recommendations for possible future research avenues to improve TCM systems accuracy, reliability, functionality, and integration.

【 授权许可】

Unknown