#10335. A Bayesian Optimized Discriminant Analysis Model for Condition Monitoring of Face Milling Cutter Using Vibration Datasets

October 2026publication date
Proposal available till 10-06-2025
4 total number of authors per manuscript0 $

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Journal’s subject area:
Sociology and Political Science;
Development;
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Abstract:
With the advent of industry 4.0, which conceptualizes self-monitoring of rotating machine parts by adopting techniques like data analytics, cloud computing, Internet of things, machine learning (ML), and artificial intelligence. The significant research area in predictive maintenance is tool condition monitoring (TCM) as the tool condition affects the overall machining process and its economics. Lately, machine learning techniques are being used to classify the tools condition in operation. These techniques are cost saving and help industries with adopting future-proof solutions for their operations. One such technique called discriminant analysis (DA) must be examined particularly for TCM. This article presents a Bayesian optimized discriminant analysis model to classify and monitor the tool condition into three user-defined classes.
Keywords:
condition monitoring; damage classification; diagnostic feature extraction; discriminant analysis; failure analysis; fault analysis; manufacturing processes; mechanical engineering; milling; offline diagnostic approaches; optimization; vibrations

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