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Comparative Study on tool fault diagnosis Methods Using Vibration Signals and cutting Force Signals by Machine Learning Technique

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Structural Durability & Health Monitoring 2020年第2期14卷 127-145页

作者： Suhas S.Aralikatti K.N.Ravikumar Hemantha Kumar H.Shivananda Nayaka V.Sugumaran NationalInstitute of Technology Karnataka Mangaluru575025India Vellore Institute of Technology University Chennai600127India

The state of cutting tool determines the quality of surface produced on the machined parts.A faulty tool produces poor sur face,inaccurate geometry and non-economic ***,it is necessary to monitor tool condition for *** process to have superior quality and economic *** the pre-sent study,fault classification of single point cutting tool for hard turning has been carried out by employing machine learning *** force and vibration signals were acquired to monitor tool condition during machining.A set of four tooling conditions namely healthy,worn flank,broken insert and extended tool overhang have been considered for the *** machine learning technique was applied to both vibration and cutting force *** wavelet features of the signals have been extracted using discrete wavelet trans formation(DWT).This transformation represents a large dataset into approximation coeffcients which contain the most useful information of the *** features,among features extracted,were selected using J48 decision tree ***-sification of tool conditions was carried out us ing Naive Bayes algorithm.A 10 fold cross validation was incorporated to test the validity of classifier.A comparison of performance of classifier was made between cutting force and vibration signal to choose the best signal acquisition method in classifying tool fault conditions using machine learning technique.

关键词： fault diagnosis of cutting tool Naive Bayes classifer,decision tree technique

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