Cutting tool wear classification and detection using multi-sensor signals and Mahalanobis-Taguchi System

Research output: Contribution to journalArticle

8 Citations (Scopus)

Abstract

The detection of cutting tool wear during a machining process is one of the most important considerations in automated manufacturing systems. This study presents a new approach for classification and detection of tool wear in milling process using multi-sensor signals and Mahalanobis-Taguchi system (MTS). The MTS is one of the decision making and pattern recognition systems frequently used to solve a multidimensional system and integrating information to construct reference scales by creating individual measurement scales for each class. These measurement scales are based upon the Mahalanobis distance (MD) for each sample. Orthogonal arrays (OA) and signal-to-noise (SN) ratio are used to identify variables of importance, and these variables are used to construct a reduced model of the measurement scale. Mahalanobis distance (MD) values were calculated based upon the feature data set extracted from the six channels of machining signals under sharp cutting tool, medium wear and critical wear conditions. Experimental data of end milling AISI P20+Ni tool steel is used to construct Mahalanobis space, to optimize and validate the system. The results show that the medium wear and critical wear stages of cutting tool conditions can be successfully detected in real-time.

Original languageEnglish
Pages (from-to)1759-1765
Number of pages7
JournalWear
Volume376-377
DOIs
Publication statusPublished - 15 Apr 2017

Fingerprint

Cutting tools
Wear of materials
sensors
Sensors
machining
Machining
Pattern recognition systems
Tool steel
decision making
pattern recognition
Signal to noise ratio
signal to noise ratios
manufacturing
Decision making
steels

Keywords

  • Mahalanobis-Taguchi System
  • Multi-sensors
  • Tool wear detection

ASJC Scopus subject areas

  • Condensed Matter Physics
  • Mechanics of Materials
  • Surfaces and Interfaces
  • Surfaces, Coatings and Films
  • Materials Chemistry

Cite this

@article{cf50625d8880456f8bb13f0e9fdb63b2,
title = "Cutting tool wear classification and detection using multi-sensor signals and Mahalanobis-Taguchi System",
abstract = "The detection of cutting tool wear during a machining process is one of the most important considerations in automated manufacturing systems. This study presents a new approach for classification and detection of tool wear in milling process using multi-sensor signals and Mahalanobis-Taguchi system (MTS). The MTS is one of the decision making and pattern recognition systems frequently used to solve a multidimensional system and integrating information to construct reference scales by creating individual measurement scales for each class. These measurement scales are based upon the Mahalanobis distance (MD) for each sample. Orthogonal arrays (OA) and signal-to-noise (SN) ratio are used to identify variables of importance, and these variables are used to construct a reduced model of the measurement scale. Mahalanobis distance (MD) values were calculated based upon the feature data set extracted from the six channels of machining signals under sharp cutting tool, medium wear and critical wear conditions. Experimental data of end milling AISI P20+Ni tool steel is used to construct Mahalanobis space, to optimize and validate the system. The results show that the medium wear and critical wear stages of cutting tool conditions can be successfully detected in real-time.",
keywords = "Mahalanobis-Taguchi System, Multi-sensors, Tool wear detection",
author = "M. Rizal and {A Ghani}, Jaharah and Nuawi, {Mohd. Zaki} and {Che Haron}, {Che Hassan}",
year = "2017",
month = "4",
day = "15",
doi = "10.1016/j.wear.2017.02.017",
language = "English",
volume = "376-377",
pages = "1759--1765",
journal = "Wear",
issn = "0043-1648",
publisher = "Elsevier BV",

}

TY - JOUR

T1 - Cutting tool wear classification and detection using multi-sensor signals and Mahalanobis-Taguchi System

AU - Rizal, M.

AU - A Ghani, Jaharah

AU - Nuawi, Mohd. Zaki

AU - Che Haron, Che Hassan

PY - 2017/4/15

Y1 - 2017/4/15

N2 - The detection of cutting tool wear during a machining process is one of the most important considerations in automated manufacturing systems. This study presents a new approach for classification and detection of tool wear in milling process using multi-sensor signals and Mahalanobis-Taguchi system (MTS). The MTS is one of the decision making and pattern recognition systems frequently used to solve a multidimensional system and integrating information to construct reference scales by creating individual measurement scales for each class. These measurement scales are based upon the Mahalanobis distance (MD) for each sample. Orthogonal arrays (OA) and signal-to-noise (SN) ratio are used to identify variables of importance, and these variables are used to construct a reduced model of the measurement scale. Mahalanobis distance (MD) values were calculated based upon the feature data set extracted from the six channels of machining signals under sharp cutting tool, medium wear and critical wear conditions. Experimental data of end milling AISI P20+Ni tool steel is used to construct Mahalanobis space, to optimize and validate the system. The results show that the medium wear and critical wear stages of cutting tool conditions can be successfully detected in real-time.

AB - The detection of cutting tool wear during a machining process is one of the most important considerations in automated manufacturing systems. This study presents a new approach for classification and detection of tool wear in milling process using multi-sensor signals and Mahalanobis-Taguchi system (MTS). The MTS is one of the decision making and pattern recognition systems frequently used to solve a multidimensional system and integrating information to construct reference scales by creating individual measurement scales for each class. These measurement scales are based upon the Mahalanobis distance (MD) for each sample. Orthogonal arrays (OA) and signal-to-noise (SN) ratio are used to identify variables of importance, and these variables are used to construct a reduced model of the measurement scale. Mahalanobis distance (MD) values were calculated based upon the feature data set extracted from the six channels of machining signals under sharp cutting tool, medium wear and critical wear conditions. Experimental data of end milling AISI P20+Ni tool steel is used to construct Mahalanobis space, to optimize and validate the system. The results show that the medium wear and critical wear stages of cutting tool conditions can be successfully detected in real-time.

KW - Mahalanobis-Taguchi System

KW - Multi-sensors

KW - Tool wear detection

UR - http://www.scopus.com/inward/record.url?scp=85020764033&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=85020764033&partnerID=8YFLogxK

U2 - 10.1016/j.wear.2017.02.017

DO - 10.1016/j.wear.2017.02.017

M3 - Article

AN - SCOPUS:85020764033

VL - 376-377

SP - 1759

EP - 1765

JO - Wear

JF - Wear

SN - 0043-1648

ER -