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Research Papers

A Novel Generalized Approach for Real-Time Tool Condition Monitoring

[+] Author and Article Information
Mahmoud Hassan

Department of Mechanical Engineering,
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

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

Vincent Thomson

Department of Mechanical Engineering,
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 140(2), 021010 (Dec 18, 2017) (8 pages) Paper No: MANU-17-1215; doi: 10.1115/1.4037553 History: Received April 01, 2017; Revised July 24, 2017

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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Figures

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Fig. 1

Normalized filtered resultant force of fresh and worn tool. V = 14,000 rpm, F = 3500 mm/min, and ad = 3 mm.

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Fig. 2

Worn tools: (a) T1, uniform VB = 0.29 mm and (b) T2, VB = 0.27 mm

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Fig. 3

Normalized filtered (a) resultant force and (b) resultant current. V = 14,000 rpm, F = 3500 mm/min, and ad = 3 mm.

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Fig. 4

Features extracted from tool T1 of resultant force signals: (a) before processing and (b) after processing, and features extracted from resultant current signals: (c) before processing and (d) after processing

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Fig. 5

Total MCE percentage in the LDA model of the current signals extracted features using 12 mm tool tests

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Fig. 6

LDA classification accuracy for 12 mm tool using current signals

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