Research Papers

Robust Tool Wear Monitoring Using Systematic Feature Selection in Turning Processes With Consideration of Uncertainties

[+] Author and Article Information
Bin Zhang

School of Mechanical Engineering,
Purdue University,
West Lafayette, IN 47907
e-mail: zhan1881@purdue.edu

Christopher Katinas

School of Mechanical Engineering,
Purdue University,
West Lafayette, IN 47907
e-mail: ckatinas@purdue.edu

Yung C. Shin

Fellow ASME
School of Mechanical Engineering,
Purdue University,
West Lafayette, IN 47907
e-mail: shin@purdue.edu

1Corresponding author.

Manuscript received November 9, 2017; final manuscript received May 2, 2018; published online June 4, 2018. Assoc. Editor: Dragan Djurdjanovic.

J. Manuf. Sci. Eng 140(8), 081010 (Jun 04, 2018) (12 pages) Paper No: MANU-17-1696; doi: 10.1115/1.4040267 History: Received November 09, 2017; Revised May 02, 2018

This paper describes a robust tool wear monitoring scheme for turning processes using low-cost sensors. A feature normalization scheme is proposed to eliminate the dependence of signal features on cutting conditions, cutting tools, and workpiece materials. In addition, a systematic feature selection procedure in conjunction with automated signal preprocessing parameter selection is presented to select the feature set that maximizes the performance of the predictive tool wear model. The tool wear model is built using a type-2 fuzzy basis function network (FBFN), which is capable of estimating the uncertainty bounds associated with tool wear measurement. Experimental results show that the tool wear model built with the selected features exhibits high accuracy, generalized applicability, and exemplary robustness: The model trained using 4140 steel turning test data could predict the tool wear for Inconel 718 turning with a root-mean-square error (RMSE) of 7.80 μm and requests tool changes with a 6% margin on average. Furthermore, the developed method was successfully applied to tool wear monitoring of Ti–6Al–4V alloy despite different mechanisms of tool wear, i.e., crater wear instead of flank wear.

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


Teti, R. , Jemielniak, K. , O'Donnell, G. , and Dornfeld, D. , 2010, “ Advanced Monitoring of Machining Operations,” CIRP Ann.-Manuf. Technol., 59(2), pp. 717–739. [CrossRef]
Abellan-Nebot, J. V. , and Subirón, F. R. , 2010, “ A Review of Machining Monitoring Systems Based on Artificial Intelligence Process Models,” Int. J. Adv. Manuf. Technol., 47(1–4), pp. 237–257. [CrossRef]
Sick, B. , 2002, “ On-Line and Indirect Tool Wear Monitoring in Turning With Artificial Neural Networks: A Review of More Than a Decade of Research,” Mech. Syst. Signal Process., 16(4), pp. 487–546. [CrossRef]
Grzesik, W. , 2008, “ Influence of Tool Wear on Surface Roughness in Hard Turning Using Differently Shaped Ceramic Tools,” Wear, 265(3–4), pp. 327–335. [CrossRef]
Niaki, F. A. , and Mears, L. , 2017, “ A Comprehensive Study on the Effects of Tool Wear on Surface Roughness, Dimensional Integrity and Residual Stress in Turning IN718 Hard-to-Machine Alloy,” J. Manuf. Processes, 30, pp. 268–280. [CrossRef]
Liu, T.-I. , and Jolley, B. , 2015, “ Tool Condition Monitoring (TCM) Using Neural Networks,” Int. J. Adv. Manuf. Technol., 78(9–12), pp. 1999–2007. [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]
Li, N. , Chen, Y. , Kong, D. , and Tan, S. , 2017, “ Force-Based Tool Condition Monitoring for Turning Process Using V-Support Vector Regression,” Int. J. Adv. Manuf. Technol., 91(1–4), pp. 351–361. [CrossRef]
Scheffer, C. , and Heyns, P. , 2001, “ Wear Monitoring in Turning Operations Using Vibration and Strain Measurements,” Mech. Syst. Signal Process., 15(6), pp. 1185–1202. [CrossRef]
Dimla, D. E. , 2002, “ The Correlation of Vibration Signal Features to Cutting Tool Wear in a Metal Turning Operation,” Int. J. Adv. Manuf. Technol., 19(10), pp. 705–713. [CrossRef]
Alonso, F. , and Salgado, D. , 2008, “ Analysis of the Structure of Vibration Signals for Tool Wear Detection,” Mech. Syst. Signal Process., 22(3), pp. 735–748. [CrossRef]
Prasad, B. S. , and Babu, M. P. , 2017, “ Correlation Between Vibration Amplitude and Tool Wear in Turning: Numerical and Experimental Analysis,” Eng. Sci. Technol., Int. J., 20(1), pp. 197–211. [CrossRef]
Li, X. , 2002, “ A Brief Review: Acoustic Emission Method for Tool Wear Monitoring During Turning,” Int. J. Mach. Tools Manuf., 42(2), pp. 157–165. [CrossRef]
Ren, Q. , Balazinski, M. , Baron, L. , Jemielniak, K. , Botez, R. , and Achiche, S. , 2014, “ Type-2 Fuzzy Tool Condition Monitoring System Based on Acoustic Emission in Micromilling,” Inf. Sci., 255, pp. 121–134. [CrossRef]
Maia, L. H. A. , Abrao, A. M. , Vasconcelos, W. L. , Sales, W. F. , and Machado, A. R. , 2015, “ A New Approach for Detection of Wear Mechanisms and Determination of Tool Life in Turning Using Acoustic Emission,” Tribol. Int., 92, pp. 519–532. [CrossRef]
Axinte, D. , and Gindy, N. , 2004, “ Assessment of the Effectiveness of a Spindle Power Signal for Tool Condition Monitoring in Machining Processes,” Int. J. Prod. Res., 42(13), pp. 2679–2691. [CrossRef]
Drouillet, C. , Karandikar, J. , Nath, C. , Journeaux, A.-C. , El Mansori, M. , and Kurfess, T. , 2016, “ Tool Life Predictions in Milling Using Spindle Power With the Neural Network Technique,” J. Manuf. Processes, 22, pp. 161–168. [CrossRef]
Zhu, K. , San Wong, Y. , and Hong, G. S. , 2009, “ Wavelet Analysis of Sensor Signals for Tool Condition Monitoring: A Review and Some New Results,” Int. J. Mach. Tools Manuf., 49(7–8), pp. 537–553. [CrossRef]
Niaki, F. A. , Feng, L. , Ulutan, D. , and Mears, L. , 2016, “ A Wavelet-Based Data-Driven Modelling for Tool Wear Assessment of Difficult to Machine Materials,” Int. J. Mechatronics Manuf. Syst., 9(2), pp. 97–121.
Subrahmanya, N. , and Shin, Y. C. , 2008, “ Automated Sensor Selection and Fusion for Monitoring and Diagnostics of Plunge Grinding,” ASME J. Manuf. Sci. Eng., 130(3), p. 031014. [CrossRef]
Segreto, T. , Simeone, A. , and Teti, R. , 2013, “ Multiple Sensor Monitoring in Nickel Alloy Turning for Tool Wear Assessment Via Sensor Fusion,” Procedia CIRP, 12, pp. 85–90. [CrossRef]
Guyon, I. , and Elisseeff, A. , 2003, “ An Introduction to Variable and Feature Selection,” J. Mach. Learn. Res., 3, pp. 1157–1182. http://www.jmlr.org/papers/volume3/guyon03a/guyon03a.pdf
Liao, T. W. , 2010, “ Feature Extraction and Selection From Acoustic Emission Signals With an Application in Grinding Wheel Condition Monitoring,” Eng. Appl. Artif. Intell., 23(1), pp. 74–84. [CrossRef]
Shi, D. , and Gindy, N. N. , 2007, “ Tool Wear Predictive Model Based on Least Squares Support Vector Machines,” Mech. Syst. Signal Process., 21(4), pp. 1799–1814. [CrossRef]
Wu, D. , Jennings, C. , Terpenny, J. , Gao, R. X. , and Kumara, S. , 2017, “ A Comparative Study on Machine Learning Algorithms for Smart Manufacturing: Tool Wear Prediction Using Random Forests,” ASME J. Manuf. Sci. Eng., 139(7), p. 071018. [CrossRef]
Wang, G. , and Cui, Y. , 2013, “ On Line Tool Wear Monitoring Based on Auto Associative Neural Network,” J. Intell. Manuf., 24(6), pp. 1085–1094. [CrossRef]
D'Addona, D. M. , Ullah, A. S. , and Matarazzo, D. , 2017, “ Tool-Wear Prediction and Pattern-Recognition Using Artificial Neural Network and DNA-Based Computing,” J. Intell. Manuf., 28(6), pp. 1285–1301. [CrossRef]
Gajate, A. , Haber, R. , Del Toro, R. , Vega, P. , and Bustillo, A. , 2012, “ Tool Wear Monitoring Using Neuro-Fuzzy Techniques: A Comparative Study in a Turning Process,” J. Intell. Manuf., 23(3), pp. 869–882. [CrossRef]
Mehrabi, M. G. , and Kannatey-Asibu Jr. , E. , 2002, “ Hidden Markov Model-Based Tool Wear Monitoring in Turning,” ASME J. Manuf. Sci. Eng., 124(3), pp. 651–658. [CrossRef]
Yu, J. , Liang, S. , Tang, D. , and Liu, H. , 2017, “ A Weighted Hidden Markov Model Approach for Continuous-State Tool Wear Monitoring and Tool Life Prediction,” Int. J. Adv. Manuf. Technol., 91(1–4), pp. 201–211. [CrossRef]
Wang, G. , Qian, L. , and Guo, Z. , 2013, “ Continuous Tool Wear Prediction Based on Gaussian Mixture Regression Model,” Int. J. Adv. Manuf. Technol., 66(9–12), pp. 1921–1929. [CrossRef]
Penedo, F. , Haber, R. E. , Gajate, A. , and del Toro, R. M. , 2012, “ Hybrid Incremental Modeling Based on Least Squares and Fuzzy K-NN for Monitoring Tool Wear in Turning Processes,” IEEE Trans. Ind. Inf., 8(4), pp. 811–818. [CrossRef]
Chungchoo, C. , and Saini, D. , 2002, “ On-Line Tool Wear Estimation in CNC Turning Operations Using Fuzzy Neural Network Model,” Int. J. Mach. Tools Manuf., 42(1), pp. 29–40. [CrossRef]
Kalpakjian, S. , 1991, Manufacturing Processes for Engineering Materials, 2nd ed., Addison-Wesley, Reading, MA.
Ren, Q. , Balazinski, M. , and Baron, L. , 2009, “ Uncertainty Prediction for Tool Wear Condition Using Type-2 Tsk Fuzzy Approach,” IEEE International Conference on Systems, Man and Cybernetics, San Antonio, TX, Oct. 11–14, pp. 660–665.
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]
Karandikar, J. M. , Abbas, A. E. , and Schmitz, T. L. , 2014, “ Tool Life Prediction Using Bayesian Updating—Part 2: Turning Tool Life Using a Markov Chain Monte Carlo Approach,” Precis. Eng., 38(1), pp. 18–27. [CrossRef]
Wang, J. , Wang, P. , and Gao, R. X. , 2015, “ Enhanced Particle Filter for Tool Wear Prediction,” J. Manuf. Syst., 36, pp. 35–45. [CrossRef]
Niaki, F. A. , Michel, M. , and Mears, L. , 2016, “ State of Health Monitoring in Machining: Extended Kalman Filter for Tool Wear Assessment in Turning of IN718 Hard-to-Machine Alloy,” J. Manuf. Processes, 24, pp. 361–369. [CrossRef]
Zhang, J. , Starly, B. , Cai, Y. , Cohen, P. H. , and Lee, Y. S. , 2017, “ Particle Learning in Online Tool Wear Diagnosis and Prognosis,” J. Manuf. Processes, 28, pp. 457–463. [CrossRef]
Ngo, P. D. , and Shin, Y. C. , 2016, “ Modeling of Unstructured Uncertainties and Robust Controlling of Nonlinear Dynamic Systems Based on Type-2 Fuzzy Basis Function Networks,” Eng. Appl. Artif. Intell., 53, pp. 74–85. [CrossRef]
Wang, L.-X. , and Mendel, J. M. , 1992, “ Fuzzy Basis Functions, Universal Approximation, and Orthogonal Least-Squares Learning,” IEEE Trans. Neural Networks, 3(5), pp. 807–814. [CrossRef]
Lee, C. W. , and Shin, Y. C. , 2003, “ Construction of Fuzzy Systems Using Least-Squares Method and Genetic Algorithm,” Fuzzy Sets Syst., 137(3), pp. 297–323. [CrossRef]
Anderson, M. , Patwa, R. , and Shin, Y. C. , 2006, “ Laser-Assisted Machining of Inconel 718 With an Economic Analysis,” Int. J. Mach. Tools Manuf., 46(14), pp. 1879–1891. [CrossRef]
Dandekar, C. R. , Shin, Y. C. , and Barnes, J. , 2010, “ Machinability Improvement of Titanium Alloy (Ti–6Al–4V) Via Lam and Hybrid Machining,” Int. J. Mach. Tools Manuf., 50(2), pp. 174–182. [CrossRef]


Grahic Jump Location
Fig. 1

Overall architecture and data flow of the proposed tool wear monitoring system

Grahic Jump Location
Fig. 2

Flow chart of the coupled feature and preprocessing parameter selection

Grahic Jump Location
Fig. 3

Instrumentation diagram of the test bed

Grahic Jump Location
Fig. 4

Comparison of the mean power feature before and after normalization

Grahic Jump Location
Fig. 5

Mean spindle power with different moving average window sizes

Grahic Jump Location
Fig. 6

The correlation of y-direction vibration RMS against tool wear

Grahic Jump Location
Fig. 7

Statistical significance of y-direction vibration features (Freq Ctrd: Frequency centroid, PSD MoI: PSD moment of inertia)

Grahic Jump Location
Fig. 8

Tool wear prediction for Inconel 718 using the optimal feature set

Grahic Jump Location
Fig. 9

Comparison of the type-2 FBFN prediction upper bounds and tool wear measurement upper bounds when the tool change alert issued

Grahic Jump Location
Fig. 10

Tool change margin with respect to number of classes

Grahic Jump Location
Fig. 11

Crater wear prediction for Ti–6Al–4V



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