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research-article

A Novel Generalized Approach for Real-Time Tool Condition Monitoring

[+] Author and Article Information
Hassan Mahmoud

Department of Mechanical Engineering, McGill University, Montreal, QC, Canada
mahmoud.hassan2@mail.mcgill.ca

Sadek Ahmad A.

Member, ASME, Aerospace Structures, Materials and Manufacturing, National Research Council Canada, Montreal, QC, Canada
ahmad.sadek@nrc.ca

Attia Helmi

Fellow, ASME Aerospace Structures, Materials and Manufacturing, National Research Council Canada, Montreal, QC, Canada; Department of Mechanical Engineering, McGill University, Montreal, QC, Canada
helmi.attia@mcgill.ca
helmi.attia@nrc.ca

Thomson Vince

Department of Mechanical Engineering, McGill University, Montreal, QC, Canada
vince.thomson@mcgill.ca

1Corresponding author.

ASME doi:10.1115/1.4037553 History: Received April 01, 2017; Revised July 24, 2017

Abstract

In high speed cutting processes, late replacement of defective tools may lead to machine breakdowns and badly affect the product quality, which subsequently lead to scrap parts and high process costs. Accurate tool condition detection is essential to achieve high level of competitiveness via increasing process productivity and standardizing the quality of the produced parts. Therefore, tool condition monitoring (TCM) systems have been widely emphasized as an important principle to achieve these industrial demands. Several studies for tool condition monitoring were carried out to capture tool failure using complex conventional and artificial intelligence techniques. However, these studies suffer from the absence of standardization and generalization. Hence, this paper presents a robust and reliable processing technique for the cutting process signals to extract generalized features in time and frequency domains. The proposed technique masks the effects of the cutting conditions on the extracted features and accentuates the tool condition effect. Characterization and statistical analysis of the processed features were performed to examine their sensitivity to the tool condition. The results revealed the processing technique capability to separate the features extracted from the spindle motor current signals into two mutually exclusive clusters according to their tool condition. The statistical analysis results were employed to optimize the tool condition detection approach using linear discrimination analysis LDA model. The results indicate the capability of the processing technique to minimize the system learning effort and to detect tool wear above the threshold level with accuracy above 90%.

Copyright (c) 2017 by ASME
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