Fuzzy Neural Network Modelling for Tool Wear Estimation in Dry Milling Operation

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Published Mar 26, 2021
X. Li B. S. Lim J. H. Zhou S. Huang S. J. Phua K. C. Shaw M. J. Er

Abstract

Tool failure may result in losses in surface finish and dimensional accuracy of a finished part, or possible damage to the work piece and machine. This paper presents a Fuzzy Neural Network (FNN) which is designed and developed for machinery prognostic monitoring. The FNN is basically a multi-layered fuzzy-rule-based neural network which integrates a fuzzy logic inference into a neural network structure. The fuzzy rules help to speed up the learning process of the complex conventional neural network structure and improve the accuracy in prediction and rate of convergence. A case study for prediction of tool life in a dry milling operation is presented to demonstrate the viability of the proposed FNN for tool condition monitoring. A comparison was made in the case study on prediction performances of different models established with the same set of experimental data. It is shown that the FNN is superior to conventional Multi-Regression Models (MRM), Backpropagation Neural Networks (BPNN) and Radial Basis Function Networks (RBFN) in term of prediction accuracy and BPNN in learning speed.

How to Cite

Li, X., S. Lim, B., H. Zhou, J., Huang, S., J. Phua, S., C. Shaw, K., & J. Er, M. (2021). Fuzzy Neural Network Modelling for Tool Wear Estimation in Dry Milling Operation. Annual Conference of the PHM Society, 1(1). Retrieved from https://papers.phmsociety.org/index.php/phmconf/article/view/1403
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Keywords

damage detection, neural network, applications: manufacturing

References
(Aliustaoglu et al., 2009) C. Aliustaoglu, H. M. Ertunc, and H. Ocak. Tool wear condition monitoring using a sensor fusion model based on fuzzy inference system. Mechanical Systems and Signal Processing, 23(2):539546, February 2009.

(Altintas et al., 1989) Y. Altintas, et al, In process Detection of Tool Failure in Milling Using Cutting Force Models, edited by C. Zhou, D. Maravall and D. Ruan, New York, Heidelberg: Physica-Verlag, pp. 373-402, 2003.

(Haber et al., 2003) R. E. Haber, A. Alique, “Intelligent process supervision for predicting tool wear in machining processes”, Mechatronics Vol. 13, PP 825849, 2003 (Huang et al., 2000) P.T. Huang, and J.C. Chen, “Neural Network-Based Tool Breakage Monitoring System for End Milling Operations”, Journal of Industrial Technology, 2000.

(Ho et al., 2009) W.H. Ho, J.T. Tsai, B.T. Lin, and J.H. Chou. Adaptive network-based fuzzy inference system for prediction of surface roughness in end milling process using hybrid taguchi-genetic learning algorithm. Expert Systems with Applications, 36(2, Part 2):32163222, March 2009.

(Kohonen, 1988) T. Kohonen, Self-organization and Associative Memory, Springer-Verlag, Berlin, 1988. (Li et al., 1999) Li, X.,.Ang, C.L , Gay, R. , "An Intelligent Business Forecaster for Strategic Business Planning", Journal of Forecasting, Vol. 18, pp. 181-204, 1999.

(Li et al., 2006) X. Li, J. Zhou, H.Zeng, Y.S. Wong, G.S. Hong, “An Intelligent Predictive Engine for Milling Machine Prognostic Monitoring”, CD Proceeding of the 4 th IEEE International Conference on Industrial Informatics, Singapore, pp 1075-1080, 2006.

(Li et al., 2007) X. Li, H. Zeng, J.H. Zhou, S. Huang, T.B.Thoe, K.C. Shaw and B.S.Lim, “Milti-Modal Sensing and Correlation Modelling for Condition-based Monitoring in Milling Machine” SIMTech Technical Report, Volume 8 Number 1, pg 50-56, Jan-Mar, 2007.

(Li et al., 1996) S. Li and M. A. Elbestawi, “Tool condition monitoring in machining by fuzzy neural networks”, Journal of Dynamic Systems, Measurement, and Control, Transactions of the ASME 118, 665–672, 1996.

(Lo et al., 2003) S.P. Lo. An adaptive-network based fuzzy inference system for prediction of workpiece surface roughness in end milling. Journal of Materials Processing Technology, 142(3):665–675, December 2003.

J. Engng Ind., Vol. 111, pp. 149-157, 1989. (Beasley et al., 1993) D. Beasley, D.R. Bull and R.R. Martin. An Overview of Genetic Algorithm Fundamentals, 15(2):58-69.

(Damodar et al., 1995) D. N. Gujarati, “Basic Econometrics”, Third Edition, McGRAW-HILL INTERNATIONAL EDITIONS ,1995.

(Dong et al., 2004) J.F. Dong, Y.S. Wong and G. S, Hong, Bayesian Support Vector Classification for Machining Process monitoring, Fourth International ICSC Symposium on Engineering of Intelligent Systems, Portugal, 29 th Feb. – 3 rd Mar. 2004.

(Er et al., 2003) M. J. Er and Y. Gao, Online Adaptive Fuzzy Neural Identification and Control of Nonlinear Dynamic Systems, in Fusion of Soft Computing and Hard Computing for Autonomous Robotic Systems

(Obitko, 1998) Marek Obitko, “Roulette Wheel Selection”, http://www.obitko.com/tutorials/genetic-algorithms /selection.php, 1998 (Rumelhart et al., 1986) D.E. Rumelhart, G.E. Hinton, and R.J. Williams, Learning internal representations by error propagation, Parallel Distributed Processing, Vol. 1, MIT Press, Cambridge, 1986.

(Kosko, 1992) B. Kosko, Neural networks and fuzzy system, Prentice-Hall International Editions, 1992. (Sick, 2002) B. Sick, Review on-Line and indirect tool wear monitoring in turning with artificial neural networks: A review of more than a decade of research, Mechanical Systems and Signal Processing, 16(4), 487–546, 2002.

(Vallejo Jr et al., 2005) A. G. Vallejo Jr, J. A. N. Flores, R. M. Menendez, L. E. Sucar, C. A. Rodriguez in M. Lazo and A. Sanfeliu (Eds.): Tool-Wear Monitoring Based on Continuous Hidden Markov Models, CIARP 2005, LNCS 3773, pp. 880–890, 2005. c_Springer-Verlag Berlin Heidelberg, 2005

(Wang et al., 2001) L. Wang, M.G. Mehrabi and E.K. Jr, “Tool Wear monitoring in Reconfigurable Machining Systems Through Wavelet Analysis”, Transactions of NAMRI, pp. 399-406. 2001.
(Yamaguchi et al., 2007) T. Yamaguchi, M. Higuchi, S. Shimada and T. Kaneeda, “Tool life monitoring during the diamond turning of electroless Ni–P”, Precision Engineering, Vol 31, 196–201, 2007.

(Yang et al., 2006) L.D. Yang, J.C. Chen, H.M. Chow, and C.T. Lin. Fuzzy-netsbased in-process surface roughness adaptive control system in endmilling operations. International Journal of Advanced Manufacturing Technology, 28(3-4):236–248, 2006.

(Zhou et al., 2005) J.H. Zhou, X. Li, A. Andernroomer, H. Zeng, K.M. Goh, Y.S.Wong, G.S.Hong, “Intelligent Prediction Monitoring System for Predictive Maintenance in Manufacturing”, Proceedings of the 31 st Annual Conference of the IEEE Industrial Electronics Society –IECON’05, pp 2314-2319, 2005.

(Zhou et al., 2006) J.H. Zhou, X. Li, S.G. Han, and W.K. Ng, “Genetic Algorithms for Feature Subset Selection in Equipment Fault Diagnosis”, CD proceeding of the World Congress on Engineering Asset Management (WCEAM), 11-14, Gold Coast, Australia, pp 11141123, 2006.

(Zeng et al., 2006) H. Zeng, T.B. Thoe, X. Li, J.H. Zhou, “Multi-modal Sensing for Machine Health Monitoring in High Speed Machining”, CD Proceeding of the 4th IEEE International Conference on Industrial Informatics, 16-18 August 2006, Singapore, pp 12171222.
Section
Poster Presentations