Abstract
The customized usage of tool inserts plays an imperative role in the economics of machining operations. Eventually, any in-process defects in the cutting tool lead to deterioration of complete machining activity. Such defects are untraceable by the conventional practices of condition monitoring. The characterization of such in-process tool defects needs to be addressed smartly. This would also assist the requirement of ‘self-monitoring’ in Industry 4.0. In this context, induction of supervised Machine Learning (ML) classifiers to design empirical classification models for tool condition monitoring is presented herein. The variation in faulty and fault-free tool condition is collected in terms of vibrations during the face milling process on CNC (Computer Numerically Controlled) machine tool. The statistical approach is incorporated to extract attributes and the dimensionality of the attributes is reduced using the J48 decision tree algorithm. The various conditions of tool inserts are then classified using two supervised algorithms viz. Bayes Net and Naïve Bayes from the Bayesian family.
Highlights
A cutting tool is presumed to commit high persistence, strength, and most important repeatability in the machining operation (Grzesik 2017)
Design and training the Bayesian family algorithms for classification of tool inserts fault and its validation considering test data set Classification of predefined classes for blind data sets based on learner’s output
A novel machine learning framework for cutting tool inserts monitoring on CNC milling based on vibration analysis was successfully investigated
Summary
A cutting tool is presumed to commit high persistence, strength, and most important repeatability in the machining operation (Grzesik 2017). Thereby, any defect in the cutting tool leads to deterioration of complete machining activity. The consequences such as poorer surface finish, a discrepancy in the dimension of workpiece, substantial power consumption of drive, discontinuance of machining process, etc. The choice of conservative input factors (Depth of cutting, Speed of machining and table feed) which satisfies the utmost cutting conditions is the Abhishek D.
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