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 TCM were carried out to capture tool failure using complex conventional and artificial intelligence (AI) 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%.
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February 2018
Research-Article
A Novel Generalized Approach for Real-Time Tool Condition Monitoring
Mahmoud Hassan,
Mahmoud Hassan
Department of Mechanical Engineering,
McGill University,
Montreal, QC H3A 0C3, Canada
e-mail: mahmoud.hassan2@mail.mcgill.ca
McGill University,
Montreal, QC H3A 0C3, Canada
e-mail: mahmoud.hassan2@mail.mcgill.ca
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Ahmad Sadek,
Ahmad Sadek
Mem. ASME
Aerospace Structures, Materials and
Manufacturing,
National Research Council Canada,
Montreal, QC H3T 2B2, Canada
e-mail: ahmad.sadek@nrc.ca
Aerospace Structures, Materials and
Manufacturing,
National Research Council Canada,
Montreal, QC H3T 2B2, Canada
e-mail: ahmad.sadek@nrc.ca
Search for other works by this author on:
M. H. Attia,
M. H. Attia
Fellow ASME
Aerospace Structures, Materials
and Manufacturing,
National Research Council Canada,
Montreal, QC H3T 2B2, Canada;
Department of Mechanical Engineering,
McGill University,
Montreal, QC H3A 0C3, Canada
e-mails: helmi.attia@mcgill.ca;
helmi.attia@nrc.ca
Aerospace Structures, Materials
and Manufacturing,
National Research Council Canada,
Montreal, QC H3T 2B2, Canada;
Department of Mechanical Engineering,
McGill University,
Montreal, QC H3A 0C3, Canada
e-mails: helmi.attia@mcgill.ca;
helmi.attia@nrc.ca
Search for other works by this author on:
Vincent Thomson
Vincent Thomson
Department of Mechanical Engineering,
McGill University,
Montreal, QC H3A 0C3, Canada
e-mail: vince.thomson@mcgill.ca
McGill University,
Montreal, QC H3A 0C3, Canada
e-mail: vince.thomson@mcgill.ca
Search for other works by this author on:
Mahmoud Hassan
Department of Mechanical Engineering,
McGill University,
Montreal, QC H3A 0C3, Canada
e-mail: mahmoud.hassan2@mail.mcgill.ca
McGill University,
Montreal, QC H3A 0C3, Canada
e-mail: mahmoud.hassan2@mail.mcgill.ca
Ahmad Sadek
Mem. ASME
Aerospace Structures, Materials and
Manufacturing,
National Research Council Canada,
Montreal, QC H3T 2B2, Canada
e-mail: ahmad.sadek@nrc.ca
Aerospace Structures, Materials and
Manufacturing,
National Research Council Canada,
Montreal, QC H3T 2B2, Canada
e-mail: ahmad.sadek@nrc.ca
M. H. Attia
Fellow ASME
Aerospace Structures, Materials
and Manufacturing,
National Research Council Canada,
Montreal, QC H3T 2B2, Canada;
Department of Mechanical Engineering,
McGill University,
Montreal, QC H3A 0C3, Canada
e-mails: helmi.attia@mcgill.ca;
helmi.attia@nrc.ca
Aerospace Structures, Materials
and Manufacturing,
National Research Council Canada,
Montreal, QC H3T 2B2, Canada;
Department of Mechanical Engineering,
McGill University,
Montreal, QC H3A 0C3, Canada
e-mails: helmi.attia@mcgill.ca;
helmi.attia@nrc.ca
Vincent Thomson
Department of Mechanical Engineering,
McGill University,
Montreal, QC H3A 0C3, Canada
e-mail: vince.thomson@mcgill.ca
McGill University,
Montreal, QC H3A 0C3, Canada
e-mail: vince.thomson@mcgill.ca
Manuscript received April 1, 2017; final manuscript received July 24, 2017; published online December 18, 2017. Assoc. Editor: Tony Schmitz.
J. Manuf. Sci. Eng. Feb 2018, 140(2): 021010 (8 pages)
Published Online: December 18, 2017
Article history
Received:
April 1, 2017
Revised:
July 24, 2017
Citation
Hassan, M., Sadek, A., Attia, M. H., and Thomson, V. (December 18, 2017). "A Novel Generalized Approach for Real-Time Tool Condition Monitoring." ASME. J. Manuf. Sci. Eng. February 2018; 140(2): 021010. https://doi.org/10.1115/1.4037553
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