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Article

Modeling of Complex Manufacturing Processes by Hierarchical Fuzzy Basis Function Networks With Application to Grinding Processes

[+] Author and Article Information
Cheol W. Lee, Yung C. Shin

School of Mechanical Engineering, Purdue University, West Lafayette, IN 47907

J. Dyn. Sys., Meas., Control 126(4), 880-890 (Mar 11, 2005) (11 pages) doi:10.1115/1.1849247 History: Received July 29, 2001; Revised January 21, 2004; Online March 11, 2005
Copyright © 2004 by ASME
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References

Liao,  T. W., and Chen,  L. J., 1994, “A Neural Network Approach for Grinding Processes: Modelling and Optimization,” Int. J. Mach. Tools Manuf., 34, pp. 919–937.
Midha,  P. S., Zhu,  C. B., and Trmal,  G. J., 1991, “Optimum Selection of Grinding Parameters Using Process Modelling and Knowledge Based System Approach,” J. Mater. Process. Technol., 28, pp. 189–198.
Xiao, G., Malkin, S., and Danai, K., 1992, “Intelligent Control of Cylindrical Plunge Grinding,” Proceedings of the ACC, Chicago, IL, 24–26 June, pp. 391–398.
Broomhead,  D. S., and Lowe,  D., 1988, “Multivariable Functional Interpolation and Adaptive Networks,” Complex Syst., 2, pp. 321–355.
Koo, T.-K. J., 1996, “Construction of Fuzzy Linguistic Model,” in Proc. of the 35th Conf. on Decision and Control, Kobe, Japan, December, pp. 98–103.
Nie,  J., and Linkens,  D. A., 1993, “Learning Control Using Fuzzified Self-Organizing Radial Basis Function Networks,” IEEE Trans. Fuzzy Syst., 1, pp. 280–287.
Shimojima, K., Fukuda, T., and Hasegawa, Y., 1995, “RBF-Fuzzy System With GA Based Unsupervised/Supervised Learning Method,” in Proc. of 1995 IEEE Int. Conf. on Fuzzy Systems, Yokohama, Japan, 20–24 March, pp. 253–258.
Wang, L. X., and Mendel, J. M., 1992a, “Back-Propagation Fuzzy Systems as Nonlinear Dynamic System Identifiers,” Proc. IEEE 1992 Int. Conf. Fuzzy Systems, San Diego, CA, pp. 1409–1418.
Wang,  L. X., and Mendel,  J. M., 1992b, “Fuzzy Basis Functions, Universal Approximation, and Orthogonal Least-Squares Learning,” IEEE Trans. Neural Netw., 3, pp. 807–814.
Glorennec, P. Y., 1994, “Learning Algorithms for Neuro-Fuzzy Networks,” in Fuzzy Control Systems, A. Kandel and G. Langholz, eds., CRC Press, Boca Raton, pp. 3–18.
Jang,  J.-S. R., 1993, “ANFIS: Adaptive-Network-Based Fuzzy Inference System,” IEEE Trans. Syst. Man Cybern., 23, pp. 665–685.
Elanayar, S. V. T., and Shin, Y. C., 1990, “Machining Condition Monitoring for Automation Using Neural Networks,” ASME Winter Annual. Mtg., PED-Vol. 44, pp. 85–95.
Venk,  S., Govind,  R., and Merchant,  E., 1990, “An Expert System Approach to Optimization of the Centerless Grinding Process,” Ann. CIRP,39, pp. 489–492.
Tönshoff,  H. K., Peters,  J., Inasaki,  I., and Paul,  T., 1992, “Modelling and Simulation of Grinding Processes,” Ann. CIRP,41, pp. 677–688.
Malkin, S., 1989, Grinding Technology: Theory and Application of Machining with Abrasives, Society of Manufacturing Engineers, Reading, MI.
Steffens,  K., 1983, “Closed Loop Simulation of Grinding,” Ann. CIRP,32/1, pp. 255–259.
Kruszynski,  B., 1991, “An Attempt to Predict Residual Stresses in Grinding of Metals With the Aid of a New Grinding Parameter,” Ann. CIRP,40/1, pp. 335–337.
Li,  Y. Y., and Chen,  Y., 1989, “Simulation of Surface Grinding Process,” Trans. ASME, 111, pp. 46–53.
Rowe,  W. B., Pettit,  J. A., Boyle,  A., and Moruzzi,  J. L., 1988, “Avoidance of Thermal Damage in Grinding and Prediction of the Damage Threshold,” Ann. CIRP,37/1, pp. 327–330.
Lindsay,  R. P., 1983, “The Effect of Wheel Wear Rate on the Grinding Performance of Three Wheel Grades,” Ann. CIRP,32/1, pp. 247–249.
Malkin,  S., 1976, “Selection of Operating Parameters in Surface Grinding of Steels,” Trans. ASME, J. Eng. Ind.,98, pp. 56–62.
Peters, J., Snoeys, R., and Maris, M., 1978, “Residual Stresses in Grinding,” AGARD Conference Preceedings No. 256, pp. 2-1–2-15.
Snoeys,  R., Maris,  M., and Peters,  J., 1978, “Thermally Induced Damage in Grinding,” Ann. CIRP,27/2, pp. 571–581.
Snoeys,  R., and Peters,  J., 1974, “The Significance of Chip Thickness in Grinding,” Ann. CIRP,23/2, pp. 227–237.
Rowe,  W. B., Yan,  L., Inasaki,  I., and Malkin,  S., 1994, “Application of Artificial Intelligence in Grinding,” Ann. CIRP,43/2, pp. 521–531.
Vishnupad, P. S., and Shin, Y. C., 1998, “Intelligent Optimization of Grinding Processes Using Fuzzy Logic,” Proceedings of the Institutions of Mechanical Engineers, Part B, Journal of Engineering Manufacture, Vol. 212, No. B8, pp. 647–660.
Kóczy,  L. T., Hirota,  K., and Muresan,  L., 1999, “Interpolation in Hierarchical Fuzzy Rule Bases,” Int. J. Fuzzy Syst.,1, pp. 77–84.
Chen,  S., Cowan,  C. F. N., and Grand,  P. M., 1991, “Orthogonal Least Squares Learning Algorithm for Radial Basis Function Networks,” IEEE Trans. Neural Netw., 2, pp. 302–309.
Hohensohn, J., and Mendel, J. M., 1994, “Two-Pass Orthogonal Least-Squares Algorithm to Train and Reduce Fuzzy Logic Systems,” Proc. of the 3rd IEEE Conf. on Fuzzy Systems, Orlando, FL, pp. 696–700.
Goldberg, D. E., 1989, Genetic Algorithms in Search, Optimization, and Machine Learning, Addison-Wesley, Reading, MA.
Michalewicz, Z., 1996, Genetic Algorithms+Data Structures=Evolution Programs, 3rd ed., Springer-Verlag, Reading, Berlin.
Chen, S., Wu, Y., and Alkadhimi, K., 1995, “A Two-Layer Learning Method for Radial Basis Function Networks Using Combined Genetic and Regularized OLS Algorithms,” Genetic Algorithms in Engineering Systems: Innovations and Applications, IEE Conf. Publication No. 414, pp. 245–249.
Lin, C.-T., and Lee, C. S. G., 1996, Neural Fuzzy Systems: A Neuro-Fuzzy Synergism to Intelligent Systems, PTR Prentice–Hall, Reading, NJ.
Demuth, H., and Beale, M., 1994, Neural Network Toolbox User’s Guide, MA, The MathWorks, Inc.
Kovach, J., 1987, “Improved Grinding of Ceramic Components,” AFWAL-TR-87-4137, 1–5 June, Dayton, OH, Vol. 7, pp. 72–100.
Shin,  Y. C., and Vishnupad,  P., 1996, “Neuro-Fuzzy Control of Complex Manufacturing Processes,” Int. J. Prod. Res., 34, pp. 3291–3309.

Figures

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FBFN with four fuzzy rules
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Convergence of GA runs when the ALS algorithm was applied to Example 1
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A comparison of training and testing errors between the ALS algorithm and backpropagation algorithm applied to Example 1
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Network structure for Example 2
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A comparison of training and testing errors between the HFBFN and NNs trained by the ALS algorithm and L–M algorithm, respectively
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Hierarchical structure of the maximum residual stress model
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A comparison between outputs from the regression model and experimental data for the residual stress
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A comparison between outputs from the FBFN model and experimental data for the residual stress
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Validation of the FBFN surface roughness model for 16 sets of grinding conditions
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Validation of the FBFN maximum residual stress model for 16 sets of grinding conditions
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A comparison between the prediction by the regression model and experimental data for the residual stress
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Fuzzy membership functions for the input variable sd
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Fuzzy membership functions for the input variable ad
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Fuzzy membership functions for the input variable vw
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Fuzzy membership functions for the input variable st
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Singleton fuzzy membership functions for the output variable Ra

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