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Comparative Study on Tool Fault Diagnosis Methods Using Vibration Signalsand Cutting Force Signals by Machine Learning Technique

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The article was published on 2020-01-01 and is currently open access. It has received 17 citations till now. The article focuses on the topics: Fault (power engineering).

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Journal ArticleDOI

A machine learning approach for vibration-based multipoint tool insert health prediction on vertical machining centre (VMC)

TL;DR: An ML-based approach to monitor the multipoint tool insert health is presented and various tool conditions were categorized using six different ‘supervised-tree-based’ algorithms and a comparative study is presented to find the best possible classifier.
Journal ArticleDOI

Application of Bayesian Family Classifiers for Cutting Tool Inserts Health Monitoring on CNC Milling

TL;DR: Induction of supervised Machine Learning (ML) classifiers to design empirical classification models for tool condition monitoring is presented herein and would assist the requirement of ‘self-monitoring’ in Industry 4.0.
Journal ArticleDOI

Classification of gear faults in internal combustion (IC) engine gearbox using discrete wavelet transform features and K star algorithm

TL;DR: K-star algorithm and discrete wavelet transform technique can be used for diagnosing the gear faults in IC engine gearbox using vibration signals.
Journal ArticleDOI

A Decision-Making Methodology Based on Expert Systems Applied to Machining Tools Condition Monitoring

TL;DR: In this paper , a new methodology for monitoring the work conditions of machining tools, based on expert systems that incorporate a reinforcement strategy into their knowledge base, was proposed and conceptualized.
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