Abstract

To implement the tool condition monitoring system in a metal cutting process, it is necessary to have sensors which will be able to detect the tool conditions to initiate remedial action. There are different signals for monitoring the cutting process which may require different sensors and signal processing techniques. Each of these signals is capable of providing information about the process at different reliability level. To arrive a good, reliable and robust decision, it is necessary to integrate the features of the different signals captured by the sensors. In this paper, an attempt is made to fuse the features of acoustic emission and vibration signals captured in a precision high speed machining center for monitoring the tool conditions. Tool conditions are classified using machine learning classifiers. The classification efficiency of machine learning algorithms are studied in time-domain, frequencydomain and time-frequency domain by feature level fusion of features extracted from vibration and acoustic emission signature.

Highlights

  • Various sensors are used in tool condition monitoring to classify the tool conditions with an objective of improving the productivity in a metal cutting environment

  • In feature level fusion method, features from multiple sensors are pooled into a single set, and the combined data set is fed to the classifiers for tool condition identification

  • The statistical features from vibration and acoustic emission signal features are combined into a single matrix and given as input to the classifiers to identify the state of the tool

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Summary

INTRODUCTION

Various sensors are used in tool condition monitoring to classify the tool conditions with an objective of improving the productivity in a metal cutting environment. In tool condition monitoring systems, tool conditions are generally correlated using signals received from sensors such as sound, optical, vibration, current, force and acoustic emission (Teti et al, 2010). Acoustic Emission (AE) and vibration based techniques are found to be very effective in monitoring the tool conditions in a variety of metal cutting operations. Many researchers studied the tool conditions using vibration signatures for identifying the tool conditions for various metal cutting processes. Tool conditions in a high speed precision milling machine was studied by Krishnakumar et al (2015) using vibration signals. Zhang et al (2016) used wireless sensors and tool prognostic studies have been carried out using vibration signals. AE and vibration sensors are used to detect the tool conditions during machining. Cutting phenomenon in turning process was studied by Hase et al (2014) with the AE signal. Arun et al (2018) studied cylindrical grinding process using AE sensor

Signal processing
SENSOR FUSION
Decision level and feature level fusion
EXPERIMENTAL SETUP
MACHINE LEARNING ALGORITHMS FOR TOOL
Decision trees
Support vector machines
Artificial Neural Network
Naive Bayes
FEATURE LEVEL FUSION OF AE AND VIBRATION SIGNALS
Feature level fusion in time-domain
Feature level fusion in frequency domain
TOOL CONDITION CLASSIFICATION
SVM variants
Confusion matrix of the fused data
Performance of classifiers based on classification efficiency
Findings
CONCLUSIONS
Full Text
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