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Performance of Logistic Model Tree Classifier using Statistical Features for Fault Diagnosis of Single Point Cutting Tool


  • School of Mechanical and Building Sciences (SMBS), VIT University, Chennai Campus, Chennai - 600127, Tamil Nadu, India
  • Department of Mechanical Engineering, Amrita School of Engineering, Amrita Vishwa Vidyapeetham,Amrita University, Coimbatore – 641112, Tamil Nadu, India


Objective: A variety of tool condition monitoring techniques in modern manufacturing system plays a key role in estimating the tool wear which can save the machine downtime and increase the cutting tool utilization. Tool wear compromises dimensional accuracy and affects the precision, tolerance and surface finish. An active condition monitoring system of tool health is required for superior productivity. Method/Analysis: In this experimental study, the accelerometer was used to acquire the vibration signal during the turning operation in a lathe machine with good and fault simulated single point cutting tool. The signals are acquired for all combinations of spindle speeds, feed rates, depth of cuts and tool wear level. In this study, 3 different spindle speeds, feed rates and depth of cuts, and 4 different tool wear levels were considered. Statistical features were extracted from the acquired signal and substantial features were recognized using a decision tree algorithm. The identified substantial statistical features were considered in classifying signals using logistic model tree classifier. Findings: The classification accuracy obtained for all the signals combined (i.e., variable spindle speeds, feed rates, depth of cuts and tool wear levels) were found to be 74.27%. The classification accuracy achieved was improved through simplifying the model by considering feed rate and depth of cut as variable factor. The accuracy of the classification was found to be in the range of 82-86%. Further, the classification accuracy was found to increase to the range of 82-93%, when considering the depth of cut alone as variable factor. Application/Improvement: The utilization of logistic model tree to identify the tool wear level in a single point cutting tool during turning operation was comprehensively analysed in this study. The performance of the classifier on fault diagnosis of single point cutting tool and its improvement by reducing the complexity of the model was discussed.


Decision Tree, Feature Extraction, Logistic Model Tree, Logistic Regression, Statistical Features Tool Condition Monitoring.

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