Abstract
The coordinate measuring machine is one of the two types of digitizers most popularly used in reverse engineering. A number of factors affect the digitizing uncertainty, such as travel speeds of the probe, pitch values (sampling points), probe angles (part orientations), probe sizes, and feature sizes. A proper selection of these parameters in a digitization or automatic inspection process can improve the digitizing accuracy for a given coordinate-measuring machine. To do so, some empirical models or decision rules are required. This paper applies and compares the nonlinear regression analysis and neural network modeling methods in developing empirical models for estimating the digitizing uncertainty. The models developed in this research can aid error prediction, accuracy improvement, and operation parameter selection in computer-aided reverse engineering and automatic inspection.
This is a preview of subscription content, log in via an institution to check access.
Similar content being viewed by others
References
Alrashdan, A., Motavalli, S. and Fallahi, B. (2000) Automatic segmentation of digitized data for reverse engineering applications. IIE Transactions on Design and Manufacturing, 32, 59-69.
Balsamo, A., Marques, D. and Sartori, S. (1990) A method for thermal-deformation corrections of CMMs. Annals of CIRP, 39, 557-560.
Bosch, T., Harlow, R. and Thompson, R. L. (1998) Fundamentals of Dimensional Metrology, 3rd Edn, Delmar Publishers, Albany, NY.
Box, G. E. P. and Draper, N. R. (1987) Empirical Model-Building and Response Surfaces, John Wiley & Sons, New York.
Coit, D. W., Jackson, B. T. and Smith, A. E. (1998) Static neural network process models: considerations and case studies. International Journal of Production Research, 36(11), 2953-2967.
Daniel, C. and Wood, F. S. (1980) Fitting Equations to Data: Computer Analysis of Multifactor Data, 2nd Edn, John Wiley & Sons, New York.
Feng, C.-X. and Pandey, V. (2002) An Experimental study of the effect of digitizing parameters on digitizing uncertainty with a CMM. International Journal of Production Research, 40(3), 687-697.
Gershenfeld, N. (1999) The Nature of Mathematical Modeling, Cambridge University Press, Cambridge, England.
Hogg, R. V. and Ledolter, J. (1992) Applied Statistics for Engineers and Physical Scientists, 2nd Edn, Macmillan, New York.
Kunzmann, H., Trapet, E. and Wäldele, F. (1990) A uniform concept for calibration, acceptance test, and periodic inspection of coordinate measuring machines using reference objects. Annals of the CIRP, 39(1), 561-564.
Kreuci, J. V. (1990) CMM measurement enhancement using probe compensation algorithms. SME Technical PaperMS90-09, SME, Dearborn, MI.
Lawrence, J. (1994) Introduction to Neural Networks: Design, Theory, and Applications, 6th Edn, California Scientific Software, Nevada City, CA.
Lawrence, M. and Petterson, A. (1998) BrainMaker User's Guide and Reference Manual, 7th Edn, California Scientific Software, Nevada City, CA.
Lin, Z.-C. and Liu, Q. Y. (2000) A neural network-based algorithm that searches for measuring points of a rule surface. IIE Transactions on Design and Manufacturing, 32, 333-343.
Matinez, S. E., Smith, A. E. and Bidanda, B. (1994) Reducing waste in casting with a predictive neural model. Journal of Intelligent Manufacturing, 5(4), 277-286.
Minitab (19XX) Minitab Reference Manual, PC Version. Minitab Inc., State College, PA.
Montgomery, D. C. (2001) Design and Analysis of Experiments, 5th Edn, John Wiley & Sons, New York.
Moon, H.-S. and Na, S.-J. (1997) Optimum design based on mathematical model and neural network to predict weld parameters for fillet joints. Journal of Manufacturing Systems, 16(1), 13-23.
Petri, K. L., Billo, R. E. and Bidanda, B. (1998) A neural network process model for abrasive flow machining operations. Journal of Manufacturing Systems, 17(1), 52-64.
Sartori, S. and Zhang, G. X. (1995) Geometric error measurement and compensation of machines. Annals of the CRIP, 44(2), 599-609.
Shen, Y.-L. and Zhang, X.-P. (1999) Pretravel compensation for horizontally oriented touch trigger probes with straight styli. Journal of Manufacturing Systems, 18(3), 175-186.
Smith, A. E. (1993) Predicting product quality with back propagation: A thermoplastic injection molding case study. International Journal of Advanced Manufacturing Technology, 8(4), 252-257.
Smith, A. E. and Mason, A. K. (1997) Cost estimation predictive modeling: regression versus neural network. The Engineering Economist, 42(2), 137-161.
Wang, X. and Feng, C.-X. (2001) An error prediction model for digitization with a coordinate measuring machine. Presented at the 2001 Industrial Engineering Research Conference, Dallas, TX, May.
Weckenmann, A., Knauer, M. and Kunzmann, H. (1998) The influence of measurement strategy on the uncertainty of CMM-measurements. CIRP Annals, 47(1), 451-454.
Yang, M. and Lee, E. (2000) Improved neural network models for reverse engineering. International Journal of Production Research, 38(9), 2067-2078.
Yarlagadda, P. K. D. V. (2000) Prediction of die casting process parameters by using an artificial neural network model for zinc alloy. International Journal of Production Research, 38(1), 119-139.
Yee, K. W. and Gavin, R. J. (1990) Implementing fast part probing and error compensation on machine tools. NIST Report 4447, National Institute of Standards and Technology, Gaithersburg, MD 20899.
Zhang, G., Veale, R., Charlton, T., Borchardt, B. and Hocken, R. (1985) Error compensation of coordinate measuring machines. CIRP Annals, 34(1), 445-448.
Zhang, G.-X. and Zhang, Y.-F. (1991) A method for machine geometric calibration using 1-D ball array. Annals of the CIRP, 40(1), 519-522.
Zhang, H.-C. and Huang, S. H. (1995) Applications of neural network applications in manufacturing: a state-of-the-art survey. International Journal of Production Research, 33, 705-728.
Zhou, E. P. and Harrison, D. K. (1999) Improving error compensation via a fuzzy-neural hybrid model. Journal of Manufacturing Systems, 18(5), 335-344.
Author information
Authors and Affiliations
Rights and permissions
About this article
Cite this article
Feng, CX.(., Wang, X. Digitizing uncertainty modeling for reverse engineering applications: regression versus neural networks. Journal of Intelligent Manufacturing 13, 189–199 (2002). https://doi.org/10.1023/A:1015734805987
Issue Date:
DOI: https://doi.org/10.1023/A:1015734805987