Research Papers

Tool Wear Monitoring and Alarm System Based on Pattern Recognition With Logical Analysis of Data

[+] Author and Article Information
Yasser Shaban

Department of Mathematics
and Industrial Engineering,
École Polytechnique,
C. P. 6079,
Succ. Centre-Ville,
Montréal, QC H3C3A7, Canada
e-mail: yasser.shaban@polymtl.ca

Soumaya Yacout

Department of Mathematics
and Industrial Engineering,
École Polytechnique,
C. P. 6079,
Succ. Centre-Ville,
Montréal, QC H3C3A7, Canada
e-mail: soumaya.yacout@polymtl.ca

Marek Balazinski

Department of Mechanical Engineering,
École Polytechnique,
C. P. 6079,
Succ. Centre-Ville,
Montréal, QC H3C3A7, Canada
e-mail: marek.balazinski@polymtl.ca

1Corresponding author.

Contributed by the Manufacturing Engineering Division of ASME for publication in the JOURNAL OF MANUFACTURING SCIENCE AND ENGINEERING. Manuscript received April 3, 2014; final manuscript received February 24, 2015; published online July 8, 2015. Assoc. Editor: Robert Gao.

J. Manuf. Sci. Eng 137(4), 041004 (Aug 01, 2015) (10 pages) Paper No: MANU-14-1144; doi: 10.1115/1.4029955 History: Received April 03, 2014; Revised February 24, 2015; Online July 08, 2015

This paper presents a new tool wear monitoring and alarm system that is based on logical analysis of data (LAD). LAD is a data-driven combinatorial optimization technique for knowledge discovery and pattern recognition. The system is a nonintrusive online device that measures the cutting forces and relates them to tool wear through learned patterns. It is developed during turning titanium metal matrix composites (TiMMCs). These are a new generation of materials which have proven to be viable in various industrial fields such as biomedical and aerospace. Since they are quite expensive, our objective is to increase the tool life by giving an alarm at the right moment. The proposed monitoring system is tested by using the experimental results obtained under sequential different machining conditions. External and internal factors that affect the turning process are taken into consideration. The system's alarm limit is validated and is compared to the limit obtained when the statistical proportional hazards model (PHM) is used. The results show that the proposed system that is based on using LAD detects the worn patterns and gives a more accurate alarm for cutting tool replacement.

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Grahic Jump Location
Fig. 1

Schematic diagram of experimental setup

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Fig. 2

Forces directions during turning

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Fig. 3

Wear classification and failure threshold

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Fig. 4

Schematic diagram for LAD online alarm system

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Fig. 5

Online alarm system front panel

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Fig. 6

Replacement decision for PHM-model and LAD alarm (r = 2). (a) PHM decision and LAD alarm decision for tool 1-5, (b) PHM decision and LAD alarm decision for tool 2–5, (c) PHM decision and LAD alarm decision for tool 3–6, (d) PHM decision and LAD alarm decision for tool 4–6, (e) PHM decision and LAD alarm decision for tool 5–6, and (f) LAD alarm decision for five tools LAD alarm.




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