In-process self-configuring approach to develop intelligent tool condition monitoring systems

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DOI	Resolve DOI: https://doi.org/10.1016/j.cirp.2024.04.049
Author	Search for: Hassan, Mahmoud¹ORCID identifier: https://orcid.org/0000-0001-6881-3882; Search for: Sadek, Ahmad¹ORCID identifier: https://orcid.org/0000-0002-2751-7400; Search for: Attia, Helmi¹ORCID identifier: https://orcid.org/0000-0002-4705-5311
Affiliation	National Research Council Canada. Aerospace
Format	Text, Article
Subject	cutting; machine learning; condition monitoring
Abstract	A self-configuring real-time tool condition monitoring (TCM) system for milling applications using vibration signals is introduced. A suite of signal processing and machine learning algorithms was developed to define a generalized correlation between distortion-resistant features of usable and worn tools. Using only a few seconds of learning data acquired at the early stage of tool life, the system synthesizes worn tool features in-process to define the decision-making boundaries, independent of the utilized cutting parameters, machines, and sensors. It provides high detection accuracy and reduces the lead time and cost needed for system development and calibration, introducing the plug-and-play concept to TCM.
Publication date	2024-05-01
Publisher	Elsevier
Licence	Creative Commons, Attribution-NonCommercial-NoDerivatives 4.0 International (CC BY-NC-ND 4.0) https://creativecommons.org/licenses/by-nc-nd/4.0/
In	CIRP Annals 73, no. 1 (1 May 2024): 81–84.
Language	English
Peer reviewed	Yes
Identifier	S0007850624000672
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Record identifier	00ca9a6f-070b-4fec-afa4-71698780fdf0
Record created	2024-05-15
Record modified	2025-11-06

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Date modified:: 2026-05-21