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dc.contributor.authorMadhusudana, C.K.
dc.contributor.authorGangadhar, N.
dc.contributor.authorKumar, H.
dc.contributor.authorNarendranath, S.
dc.identifier.citationSDHM Structural Durability and Health Monitoring, 2018, Vol.12, 2, pp.97-113en_US
dc.description.abstractThis paper presents the fault diagnosis of face milling tool based on machine learning approach. While machining, spindle vibration signals in feed direction under healthy and faulty conditions of the milling tool are acquired. A set of discrete wavelet features is extracted from the vibration signals using discrete wavelet transform (DWT) technique. The decision tree technique is used to select significant features out of all extracted wavelet features. C-support vector classification (C-SVC) and ?-support vector classification (?-SVC) models with different kernel functions of support vector machine (SVM) are used to study and classify the tool condition based on selected features. From the results obtained, C-SVC is the best model than ?-SVC and it can be able to give 94.5% classification accuracy for face milling of special steel alloy 42CrMo4. Copyright � 2018 Tech Science Press..en_US
dc.titleUse of discrete wavelet features and support vector machine for fault diagnosis of face milling toolen_US
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