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

Aerospace Structures, Materials and
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;

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%.

Copyright © 2018 by ASME
Your Session has timed out. Please sign back in to continue.


ISO, 1989, “ Tool Life Testing in Milling—Part 2: End Milling,” International Organization for Standardization, Geneva, Switzerland, Standard No. ISO8688-2. https://www.iso.org/standard/16092.html
Altintas, Y. , 2012, Manufacturing Automation: Metal Cutting Mechanics, Machine Tool Vibrations, and CNC Design, Cambridge University Press, New York.
Karandikar, J. M. , Abbas, A. E. , and Schmitz, T. L. , 2014, “ Tool Life Prediction Using Bayesian Updating. Part 1: Milling Tool Life Model Using a Discrete Grid Method,” Precis. Eng., 38(1), pp. 9–17. [CrossRef]
Zhang, D. , 2011, “ An Adaptive Procedure for Tool Life Prediction in Face Milling,” Proc. Inst. Mech. Eng., Part J, 225(1), pp. 1130–1136. [CrossRef]
Kalvoda, T. , and Yean-Ren, H. , 2010, “ A Cutter Tool Monitoring in Machining Process Using Hilbert–Huang Transform,” Int. J. Mach. Tools Manuf., 50(5), pp. 495–501. [CrossRef]
Xu, T. , and Feng, Z. , 2009, “ Tool Wear Identifying Based on EMD and SVM With AE Sensor,” Ninth International Conference on Proceedings of the Electronic Measurement and Instruments (ICEMI), Beijing, China, Aug. 16–19, pp. 948–952.
Chung, K. T. , and Geddam, A. , 2003, “ A Multi-Sensor Approach to the Monitoring of End Milling Operations,” J. Mater. Process. Technol., 139(1–3), pp. 15–20. [CrossRef]
Nouri, M. , Fussell, B. K. , Ziniti, B. L. , and Linder, E. , 2015, “ Real-Time Tool Wear Monitoring in Milling Using a Cutting Condition Independent Method,” Int. J. Mach. Tools Manuf., 89, pp. 1–13. [CrossRef]
Aliustaoglu, C. , Ertunc, H. M. , and Ocak, H. , 2009, “ Tool Wear Condition Monitoring Using a Sensor Fusion Model Based on Fuzzy Inference System,” Mech. Syst. Signal Process., 23(2), pp. 539–546. [CrossRef]
Siddiqui, R. A. , Amer, W. , Ahsan, Q. , Grosvenor, R. I. , and Prickett, P. W. , 2007, “ Multi-Band Infinite Impulse Response Filtering Using Microcontrollers for e-Monitoring Applications,” Microprocess. Microsyst., 31(6), pp. 370–380. [CrossRef]
Bassiuny, A. , and Li, X. , 2007, “ Flute Breakage Detection During end Milling Using Hilbert–Huang Transform and Smoothed Nonlinear Energy Operator,” Int. J. Mach. Tools Manuf., 47(6), pp. 1011–1020. [CrossRef]
Shao, H. , Wang, H. L. , and Zhao, X. M. , 2004, “ A Cutting Power Model for Tool Wear Monitoring in Milling,” Int. J. Mach. Tools Manuf., 44(14), pp. 1503–1509. [CrossRef]
Li, H. , Zeng, H. , and Chen, X. , 2006, “ An Experimental Study of Tool Wear and Cutting Force Variation in the end Milling of Inconel 718 With Coated Carbide Inserts,” J. Mater. Process. Technol., 180(1), pp. 296–304.
Altintas, Y. , 1992, “ Prediction of Cutting Forces and Tool Breakage in Milling From Feed Drive Current Measurements,” ASME J. Eng. Ind., 114(4), pp. 386–392. [CrossRef]
Wang, L. , and Gao, R. X. , 2006, Condition Monitoring and Control for Intelligent Manufacturing, Springer, London. [CrossRef]
Ravindra, H. V. , Srinivasa, Y. G. , and Krishnamurthy, R. , 1997, “ Acoustic Emission for Tool Condition Monitoring in Metal Cutting,” Wear, 212(1), pp. 78–84. [CrossRef]
Vallejo, A. J. , Morales-Menéndez, R. , and Alique, J. , 2008, “ On-Line Cutting Tool Condition Monitoring in Machining Processes Using Artificial Intelligence,” Robotics Automation and Control, InTech, Rijeka, Croatia, pp. 143–166.
Liang, S. Y. , Hecker, R. L. , and Landers, R. G. , 2002, “ Machining Process Monitoring and Control: The State-of-the-art,” J. Manuf. Sci. Eng., 126(2), pp. 297–310.
Snr, D. E. D. , 2001, “ Correlation of Cutting Force Features With Tool Wear in a Metal Turning Operation,” Proc. Inst. Mech. Eng., Part B, 215(3), pp. 435–440. [CrossRef]
Elbestawi, M. , Papazafiriou, T. , and Du, R. , 1991, “ In-Process Monitoring of Tool Wear in Milling Using Cutting Force Signature,” Int. J. Mach. Tools Manuf., 31(1), pp. 55–73. [CrossRef]
Kondo, E. , and Shimana, K. , 2012, “ Monitoring of Prefailure Phase and Detection of Tool Breakage in Micro-Drilling Operations,” Procedia CIRP, 1, pp. 581–586. [CrossRef]
Wang, G. , Yang, Y. , and Li, Z. , 2014, “ Force Sensor Based Tool Condition Monitoring Using a Heterogeneous Ensemble Learning Model,” Sensors, 14(11), pp. 21588–21602. [CrossRef] [PubMed]
Scheffer, C. , Kratz, H. , Heyns, P. S. , and Klocke, F. , 2003, “ Development of a Tool Wear-Monitoring System for Hard Turning,” Int. J. Mach. Tools Manuf., 43(10), pp. 973–985. [CrossRef]
Jonak, J. , and Gajewski, J. , 2008, “ Identification of Ripping Tool Types With the Use of Characteristic Statistical Parameters of Time Graphs,” Tunnelling Underground Space Technol., 23(1), pp. 18–24. [CrossRef]
Choi, Y. J. , Park, M. S. , and Chu, C. N. , 2008, “ Prediction of Drill Failure Using Features Extraction in Time and Frequency Domains of Feed Motor Current,” Int. J. Mach. Tools Manuf., 48(1), pp. 29–39. [CrossRef]
Mannan, M. A. , Broms, S. , and Lindström, B. , 1989, “ Monitoring and Adaptive Control of Cutting Process by Means of Motor Power and Current Measurements,” CIRP Ann. Manuf. Technol., 38(1), pp. 347–350. [CrossRef]
McLachlan, G. , 2004, Discriminant Analysis and Statistical Pattern Recognition, Wiley, Hoboken, NJ.
Hausmair, K. , Chi, S. , Singerl, P. , and Vogel, C. , 2013, “ Aliasing-Free Digital Pulse-Width Modulation for Burst-Mode RF Transmitters,” IEEE Trans. Circuits Syst. I, 60(2), pp. 415–427. [CrossRef]


Grahic Jump Location
Fig. 1

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

Grahic Jump Location
Fig. 2

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

Grahic Jump Location
Fig. 3

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

Grahic Jump Location
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

Grahic Jump Location
Fig. 5

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

Grahic Jump Location
Fig. 6

LDA classification accuracy for 12 mm tool using current signals



Some tools below are only available to our subscribers or users with an online account.

Related Content

Customize your page view by dragging and repositioning the boxes below.

Related Journal Articles
Related eBook Content
Topic Collections

Sorry! You do not have access to this content. For assistance or to subscribe, please contact us:

  • TELEPHONE: 1-800-843-2763 (Toll-free in the USA)
  • EMAIL: asmedigitalcollection@asme.org
Sign In