Determination of the minimum enclosing zone for straightness and flatness using support vector regression

Intelligent Engineering Systems Through Artificial Neural Networks

Volumn 12, Issue , 2002, Pages 937-944

  Prakasvudhisarn, Chakguy ^a   Trafalis, Theodore B ^a   Raman, Shivakumar ^a  

^a University of Oklahoma   (United States)

Author keywords

Biomechanical Measurements; Feature Detection; Function Approximation; Least Squares Fitting and Estimation; Manufacturing Applications; Manufacturing Applications and Engineering; Mechanical Design; Nonlinear Function Approximation; Optimization

Indexed keywords

COMPONENTS; FITS AND TOLERANCES; LEAST SQUARES APPROXIMATIONS; QUALITY ASSURANCE; REGRESSION ANALYSIS;

SUPPORT VECTOR MACHINES (SVM);

SPATIAL VARIABLES MEASUREMENT;

EID: 0037004820     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: None     Document Type: Conference Paper

References (17)

1. ASME Y14.5M-1994. (1995). Dimensioning and Tolerancing. New York, NY: The American Society of Mechanical Engineers.
2. Carr, K., and Ferreira, P. (1995). Verification of form tolerances Part I: Basic issues, flatness, and straightness. Precision Engineering, 17(2), 131-143.
3. Drucker, H., Burges, C.J.C., Kaufman,L., Smola, A., and Vapnik, V. (1997). Support Vector Regression Machines. In M. Mozer, M. Jordan, and T. Petsche (eds.), Advances in Neural Information Processing Systems 9, (pp. 155-161). Cambridge, MA: MIT Press.
4. Kanada, T., and Suzuki, S. (1993a). Evaluation of minimum zone flatness by means of nonlinear optimization techniques and its verification. Precision Engineering, 15(2), 93-99.
5. Kanada, T., and Suzuki, S. (1993b). Application of several computing techniques for minimum zone straightness. Precision Engineering, 15(4), 274-280.
6. Malyscheff, A.M., Trafalis, T.B., and Raman, S. (2002). From support vector machine learning to the determination of the minimum enclosing zone. Computers and Industrial Engineering, 42, 59-74.
7. Samuel, G.L., and Shunmugam, M.S. (1999). Evaluation of straightness and flatness error using computational geometric techniques. Computer Aided Design, 31 (13), 829-843.
8. Schölkopf, B., Bartlett, P.L., Smola, A., and Williamson, R. (1998). Support Vector Regression with Automatic Accuracy Control In L. Niklasson, M. Boden, and T. Ziemke (eds.), Proceedings of ICANN'98, Perspectives in Neural Computing, (pp. 111-116). Berlin: Springer Verlag.
9. Schölkopf, B., Bartlett, P.L., Smola, A., and Williamson, R. (1999). Shrinking the Tube: A new Support Vector Regression Algorithm. In M.S. Kearns, S.A. Solla, and D.A. Cohn (eds.), Advances in Neural Inf. Processing Systems. 11, (pp. 330-336). Cambridge, MA: MIT Press.
10. Shunmugam, M.S. (1987a). Comparison of linear and normal deviations of forms of engineering surfaces. Precision Engineering, 9(2), 96-102.
11. Shunmugam, M.S. (1987b). New approach for evaluating form errors of engineering surfaces. Computer-Aided Design, 19(7), 368-374.
12. Shunmugam, M.S. (1991). Establishing Reference Figures for Form Evaluation of Engineering Surfaces. Journal of Manufacturing Systems, 10(4), 314-321.
13. Smola, A.J. and Scholkopf, B. (1998). A Tutorial on Support Vector Regression. NeuroCOLT Technical Report NC-TR-98-030, Royal Holloway College, University of London, UK.
14. Traband, M.T., Joshi, S., Wysk, R.A., and Cavalier, T.M. (1989). Evaluation of Straightness and Flatness Tolerances Using the Minimum Zone. Manufacturing Review, 2(3), 189-195.
15. Vapnik, V. (1995). The Nature of Statistical Learning Theory. New York: Springer Verlag.
16. Vapnik, V., Golowich, S.E., and Smola, A. (1997). Support Vector Method for Function Approximation, Regression Estimation, and Signal Processing. In M. Mozer, M. Jordan, and T. Petsche (eds.), Advances in Neural Information Processing Systems 9, (pp.281-287). Cambridge, MA: MIT Press.
17. Wang, Y. (1992). Minimum Zone Evaluation of Form Tolerances. Manufacturing Review, 5(3), 213-220.