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Fault Diagnosis of a Single Point Cutting Tool using Statistical Features by Random Forest Classifier
Objectives: There is a wide range of methods implemented for tool condition monitoring in the erstwhile manufacturing industry to ensure that the process continues uninterruptedly with minimal supervision. This monitoring method reduces the overall maintenance cost of machinery and prevents the occurrence of failure by prediction. This prior detection of tool wear, in turn, reduces the machine downtime and enhances machining efficiency. The progressive wear of a cutting tool can be detrimental to the quality of the machined surface, tolerances, dimensional accuracy and also adversely change the work or tool geometry. So the requirement of a diagnosing system with consistency is vital. Method/Analysis: This study deals with acquiring vibrational signals using accelerometer during turning operation performed on a lathe machine with good and fault simulated single point cutting tool. From the acquired signals, certain statistical features such as standard deviation, kurtosis etc. were extracted and substantial features were recognised using a decision tree algorithm. Those recognised features were deliberated in classifying data using random forest classifier. Findings: The accuracy of classification by the random forest classifier for all the signals combined together yields 74.4%. When considering feed rate and depth of cut as varying parameters yields an accuracy around 84%. Further an accuracy of around 88% was observed when considering depth of cut as varying parameter. When considering every experiment as a separate model yields around 95% classification accuracy. Applications/Improvements: This research work analysed the utilization of random forest classifier to identify the tool wear. It can be used in identifying the tool wear which affects surface finish, dimensional accuracy and tolerance of the part during machining. This work can be improved by analysing with different classifier algorithms to efficiently predict the tool wear.
Confusion Matrix, Decision Tree, Feature Extraction, Random Forest, Statistical Features, Tool Condition Monitoring
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