Application of Bayesian Family Classifiers for Cutting Tool Inserts Health Monitoring on CNC Milling

Abhishek D. Patange and Jegadeeshwaran R
Publication Target: 
IJPHM
Publication Issue: 
2
Submission Type: 
Full Paper
AttachmentSizeTimestamp
ijphm_20_016.pdf4.2 MBJanuary 7, 2021 - 9:44am

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.

Publication Year: 
2020
Publication Volume: 
11
Publication Control Number: 
016
Page Count: 
13
Submission Keywords: 
condition monitoring
machine learning
vibration analysis
Accelerometer
Tool insert
CNC milling
time domain
descriptive statistics
Submission Topic Areas: 
Data-driven methods for fault detection, diagnosis, and prognosis
Submitted by: 
  
 
 
 

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