Rotary ultrasonic machining (RUM) is one of the cost-effective machining methods for machining difficult to process material. It is a hybrid machining process that combines the material removal mechanisms of diamond grinding with ultrasonic machining. However, due to the lack of understanding of the mechanisms of these operations, models for these machining processes are difficult to establish. In this paper, the support vector fuzzy adaptive network (SVFAN), a parameter free nonlinear regression technique, is used to model the material removal rate in RUM. The SVFAN retains the advantages of both the fuzzy adaptive networks and the support vector machines. The former possesses the linguistic representation ability and the latter is a very effective learning machine. The results are compared with that obtained by the use of fuzzy adaptive network and it is shown that the combined approach is a more effective algorithm for the modeling of complex manufacturing processes.
Issue Section:
Research Papers
1.
Hu
, P.
, Zhang
, J. M.
, Pei
, Z. J.
, and Treadwell
, C.
, 2002, “Modeling of Material Removal Rate in Rotary Ultrasonic Machining: Designed Experiments
,” J. Mater. Process. Technol.
0924-0136, 129
(1–3
), pp. 339
–344
.2.
Pei
, Z. J.
, Prabhakar
, D.
, Ferreira
, P. M.
, and Haselkorn
, M.
, 1995, “A Mechanistic Approach to Prediction of Material Removal Rates in Rotary Ultrasonic Machining
,” ASME J. Eng. Ind.
0022-0817, 117
(2
), pp. 142
–151
.3.
Prabhakar
, D.
, Pei
, Z. J.
, Ferreira
, P. M.
, and Haselkorn
, M.
, 1993, “A Theoretical Model for Predicting Material Removal Rates in Rotary Ultrasonic Machining
,” Trans. NAMRI/SME
1047-3025, 21
, pp. 167
–172
.4.
Pei
, Z. J.
, and Ferreira
, P. M.
, 1998, “Modeling of Ductile-Mode Material Removal in Ratory Ultrasonic Machining
,” Int. J. Mach. Tools Manuf.
0890-6955, 38
, pp. 1399
–1418
.5.
Zhang
, Q. H.
, Wu
, C. L.
, Sun
, J. L.
, and Jia
, Z. X.
, 2000, “Mechanism of Material Removal in Ultrasonic Drilling of Engineering Ceramics
,” Proc. Inst. Mech. Eng., Part B
0954-4054, 214
, pp. 805
–810
.6.
Ya
, G.
, Qin
, H. W.
, Yang
, S. C.
, and Xu
, Y. W.
, 2002, “Analysis of the Rotary Ultrasonic Machining Mechanism
,” J. Mater. Process. Technol.
0924-0136, 129
, pp 182
–185
.7.
Jiao
, Y.
, Pei
, Z. J.
, Lei
, S.
, Lee
, E. S.
, and Fisher
, G.
, 2005, “Fuzzy Adaptive Networks in Machining Process Modeling: Dimensional Error Prediction for Turning Operations
,” Int. J. Prod. Res.
0020-7543, 43
, pp. 2931
–2948
.8.
Shen
, J. D.
, Pei
, Z. J.
, Lee
, E. S.
, and Fisher
, G.
, 2006, “Modeling and Analysis of Waviness Reduction in Soft-Pad Grinding of Wire-Sawn Silicon Wafers by Support Vector Regression
,” Int. J. Prod. Res.
0020-7543, 44
, pp. 2605
–2623
.9.
Jiao
, Y.
, Pei
, Z. J.
, Lei
, S.
, Lee
, E. S.
, and Fisher
, G.
, 2006, “Fuzzy Adaptive Networks for Waviness Removal in Grinding of Wire-Sawn Silicon Wafers
,” ASME J. Manuf. Sci. Eng.
1087-1357, 128
, pp. 938
–943
.10.
Cheng
, C.-B.
, and Lee
, E. S.
, 1999, “Applying Fuzzy Adaptive Network to Fuzzy Regression
,” Comput. Math. Appl.
0898-1221, 38
, pp. 123
–140
.11.
Jang
, J.
, 1993, “ANFIS: Adaptive-Network-Based Fuzzy Inference System
,” IEEE Trans. Syst. Man Cybern.
0018-9472, 23
(3
), pp. 665
–685
.12.
Jiao
, Y.
, 2002, “Fuzzy Adaptive Networks and Applications to Humanistic Systems
,” Ph.D. thesis, Kansas State University, Manhattan, KS.13.
Vapnik
, V. N.
, 1995, The Nature of Statistical Learning Theory
, Springer
, New York
.14.
Vapnik
, V. N.
, 1999, “An Overview of Statistical Learning Theory
,” IEEE Trans. Neural Netw.
1045-9227, 10
(5
), pp. 988
–999
.15.
Kecman
, V.
, 2001, Learning and Soft Computing: Support Vector Machines, Neural Networks, and Fuzzy Logic Models
, MIT Press
, Cambridge, MA
.16.
Smola
, A. J.
, 1998, “Learning With Kernels
,” Ph.D. thesis, GMD, Birlinghoven, Germany.17.
Ramesh
, R.
, Mannan
, M. A.
, and Poo
, A. N.
, 2002, “Support Vector Machines Model for Classification of Thermal Error in Machine Tools
,” Int. J. Adv. Manuf. Technol.
0268-3768, 20
(2
), pp. 114
–120
.18.
Chinnam
, R.
, 2002, “Support Vector Machines for Recognizing Shifts in Correlated and Other Manufacturing Processes
,” Int. J. Prod. Res.
0020-7543, 40
(17
), pp. 4449
–4466
.19.
Kim
, J.
, and Won
, S.
, 2002, “New Fuzzy Inference System Using a Support Vector Machine
,” Proceedings of the 41st IEEE Conference on Decision and Control
, Las Vegas, NV
.20.
Chiang
, J. H.
, and Hao
, P. Y.
, 2004, “Support Vector Learning Mechanism for Fuzzy Rule-Based Modeling: A New Approach
,” IEEE Trans. Fuzzy Syst.
1063-6706, 12
, pp. 1
–12
.21.
Hong
, D. H.
, and Hwang
, C. H.
, 2003, “Support Vector Fuzzy Regression Machines
,” Fuzzy Sets Syst.
0165-0114, 138
, pp. 271
–281
.22.
Chen
, J. H.
, and Chen
, C. S.
, 2002, “Fuzzy Kernel Percetron
,” IEEE Trans. Neural Netw.
1045-9227, 13
, pp. 1364
–1373
.23.
Shen
, J. D.
, 2005, “Fusing of Support Vector Machines and Soft computing for Pattern recognition and Regression
,” Ph.D. thesis, Kansas State University, Manhattan, KS.24.
Lin
, C. T.
, Yeh
, C. M.
, Liang
, S. F.
, Chung
, J. F.
, and Kumar
, N.
, 2006, “Support Vector Based Fuzzy Neural Network for Pattern Classification
,” IEEE Trans. Fuzzy Syst.
1063-6706, 14
, pp. 31
–41
.25.
Li
, Z. C.
, Jiao
, Y.
, Deines
, T. W.
, Pei
, Z. J.
, and Treadwell
, C.
, 2005, “Rotary Ultrasonic Machining of Ceramic Matrix Composites: Feasibility Study and Design Experiments
,” Int. J. Mach. Tools Manuf.
0890-6955, 45
, pp. 1402
–1411
.26.
Legge
, P.
, 1964, “Ultrasonic Drilling of Ceramics
,” Ind. Diamond Rev.
0019-8145, 24
, pp. 20
–24
.27.
Legge
, P.
, 1966, “Machining Without Abrasive Slurry
,” Ultrasonics
0041-624X, 4
, pp. 157
–162
.28.
Spur
, G.
, and Holl
, S. E.
, 1997, “Material Removal Mechanisms During Ultrasonic Assisted Grinding
,” Annals of the German Academic Society for Production Engineering
, 4
(2
), pp. 9
–14
.29.
Jang
, J.-S. R.
, Sun
, C.-T.
, and Mizutani
, E.
, 1997, Neuro-Fuzzy and Soft Computing: A Computational Approach to Learning and Machine Intelligence
, Prentice-Hall
, Upper Saddle River, NJ
.30.
Takagi
, Y.
, and Sugeno
, M.
, 1985, “Fuzzy Identification of Systems and Its Application to Modeling and Control
,” IEEE Trans. Syst. Man Cybern.
0018-9472, 15
(1
), pp. 116
–132
.31.
Cortes
, C.
, and Vapnik
, V.
, 1995, “Support-Vector Networks
,” Mach. Learn.
0885-6125, 20
, pp. 273
–297
.32.
Hearst
, M.
, Schölkopf
, B.
, Dumais
, S.
, Osuna
, E.
, and Platt
, J.
, 1998, “Trends and Controversies—Support Vector Machines
,” IEEE Intell. Syst.
1094-7167, 13
(4
), pp. 18
–28
.33.
Smola
, A. J.
, Bartlett
, P. L.
, Scholkopf
, B.
, and Schuurmans
, D.
, 2000, Advances in Large Margin Classifiers
, The MIT Press
, Cambridge, MA
.34.
Kim
, K.
, Jung
, K.
, Park
, S.
, and Kim
, H.
, 2002, “Support Vector Machines for Texture Classification
,” IEEE Trans. Pattern Anal. Mach. Intell.
0162-8828, 24
(11
), pp. 1542
–1550
.35.
Jang
, J. S. R.
, and Sun
, C. T.
, 1993, “Functional Equivalence Between Radial Basis Function Networks and Fuzzy Inference Systems
,” IEEE Trans. Neural Netw.
1045-9227, 4
(1
), pp. 156
–159
.36.
Li
, Z. C.
, 2006, “Rotary Ultrasonic Machining of Ceramic Matrix Composites: Material Removal Rate Mechanism and Control of Machining-Induced Edge Chipping
,” Ph.D. thesis, Kansas State University, Manhattan, KS.37.
Cherkassky
, V.
, and Ma
, Y.
, 2004, “Practical Selection of SVM Parameters and Noise Estimation for SVM Regression
,” Neural Networks
0893-6080, 17
(1
), pp. 113
–126
.38.
Jiao
, Y.
, Hu
, P.
, Pei
, Z. J.
, Lei
, S.
, and Lee
, E. S.
, 2003, “Application of Fuzzy Adaptive Networks in Manufacturing: Prediction of Material Removal Rate in Rotary Ultrasonic Machining
,” Smart Engineering System Design: Neural Networks, Fuzzy Logic, Evolutionary Programming, Complex Systems and Artificial Life—Proceedings of the Artificial Neural Networks in Engineering Conference
, St. Louis, MO
, Vol. 13
, pp. 491
–496
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