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Three-dimensional modeling of coordinate measuring machines probing accuracy and settings using fuzzy knowledge bases: Application to TP6 and TP200 triggering probes

Published online by Cambridge University Press:  20 October 2011

Sofiane Achiche  and
Adam Wozniak
Sofiane Achiche*
Affiliation:
Department of Mechanical Engineering, École Polytechnique de Montréal, University of Montreal, Montreal, Quebec, Canada
Adam Wozniak
Affiliation:
Institute of Metrology and Biomedical Engineering, Faculty of Mechatronics, Warsaw University of Technology, Warsaw, Poland
*
Reprint requests to: Sofiane Achiche, Department of Mechanical Engineering, École Polytechnique de Montréal, University of Montreal, P.O. Box 6079, Station Centre-Ville 2900, Montreal, QC H3C 3A7, Canada. E-mail: sofiane.achiche@polymtl.ca
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Abstract

One of the fundamental elements that determines the precision of coordinate measuring machines (CMMs) is the probe, which locates measuring points within measurement volume. In this paper genetically generated fuzzy knowledge based models of three-dimensional (3-D) probing accuracy for one- and two-stage touch trigger probes are proposed. The fuzzy models are automatically generated using a dedicated genetic algorithm developed by the authors. The algorithm uses hybrid coding, binary for the rule base and real for the database. This hybrid coding, used with a set of specialized operators of reproduction, proved to be an effective learning environment in this case. Data collection of the measured objects' coordinates was carried out using a special setup for probe testing. The authors used a novel method that applies a low-force high-resolution displacement transducer for probe error examination in 3-D space outside the CMM measurement. The genetically generated fuzzy models are constructed for both one stage (TP6) and two stage (TP200) types of probes. First, the optimal number of settings is defined using an analysis of the influence of fuzzy rules on TP6 accuracy. Then, once the number of settings is obtained, near optimal fuzzy knowledge bases are generated for both TP6 and TP200 triggering probes, followed by analysis of the finalized fuzzy rules bases for knowledge extraction about the relationships between physical setup values and error levels of the probes. The number of fuzzy sets on each premise leads to the number of physical setups needed to get satisfactory error profiles, whereas the fuzzy rules base adds to the knowledge linking the design experiment parameters to the pretravel error of CMM machines. Satisfactory fuzzy logic equivalents of the 3-D error profiles were obtained for both TP6 and TP200 with root mean squsre errors ranging from 0.00 mm to a maximum of 0.58 mm.

Keywords

Coordinate Measuring Machine CalibrationFuzzy LogicGenetic AlgorithmsMeasurement
Type
Regular Article
Information
AI EDAM , Volume 26 , Issue 4: Intelligent Decision Support and Modeling , November 2012 , pp. 425 - 441
Copyright
Copyright © Cambridge University Press 2011

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References

REFERENCES

Achiche, S., Balazinski, M., & Baron, L. (2004). Multi-combinative strategy to avoid premature convergence in genetically-generated fuzzy knowledge mechanics. Journal of Theoretical and Applied Mechanics 42(3), 417–444.Google Scholar
Achiche, S., Baron, L., & Balazinski, M. (2003). Real/binary-like coded genetic algorithm to automatically generate fuzzy knowledge bases. Proc. Int. Conf. Control and Automation, pp. 799–803.Google Scholar
Achiche, S., Baron, L., & Balazinski, M. (2004 a). Real/binary-like coded versus binary coded genetic algorithms to automatically generate fuzzy knowledge bases: a comparative study. Engineering Applications of Artificial Intelligence 17(4), 313–325.CrossRefGoogle Scholar
Achiche, S., Baron, L., & Balazinski, M. (2004 b). Scheduling exploration/exploitation levels in genetically-generated fuzzy knowledge bases. Proc. Annual Conf. North American Fuzzy Information Processing Society, NAFIPS 1, pp. 401–406, Banff, Canada.CrossRefGoogle Scholar
Akrout, K., Baron, L., Balazinski, M., & Achiche, S. (2007). Influence of the migration process on the learning performances of fuzzy knowledge bases. Proc. NAFIPS 2007—2007 Annual Meeting of the North American Fuzzy Information Processing Society, pp. 478–483.CrossRefGoogle Scholar
ANSI/ASME. (1989). Methods for the Performance Evaluation of Coordinate Measuring Machines. ANSI/ASME B89.1.12M. New York: American Society of Mechanical Engineers.Google Scholar
Balazinski, M., Czogala, E., Mayer, J.R.R., & Shen, Y. (1997). Pre-travel compensation of kinematic touch trigger probes using fuzzy decision support system. Proc. 7th IFSA World Congr., p. 339.Google Scholar
British Standards Association. (1989). Coordinate Measuring Machines, Part 3: Code of Practice. BS 6808. London: British Standards Association.Google Scholar
Burdekin, M., & Di Giacomo, B., & Xijing, Z. (1985). Calibration software and application to coordinate measuring machine. Proc. Conf. Software for Coordinate Measuring Machines, p. 1.Google Scholar
Butler, C. (1991). An investigation into the performance of probes on coordinate measuring machines. Industrial Metrology 2(1), 59–70.CrossRefGoogle Scholar
Cauchick-Miguel, P.A., & King, T.G. (1998). Factors which influence CMM touch trigger probe performance. International Journal of Machine Tools & Manufacture 38(4), 363–374.CrossRefGoogle Scholar
Chan, F.M.M., Davis, E.J., King, T.G., & Stout, K.J. (1997). Some performance characteristics of a multi-axis touch trigger probe. Measurement Science & Technology 8(8), 837–848.CrossRefGoogle Scholar
Cordon, O., Herrera, F., & Villar, P. (2000). Analysis and guidelines to obtain a good uniform fuzzy partition granularity for fuzzy rule-based systems using simulated annealing. International Journal of Approximate Reasoning 25(3), 187–215.CrossRefGoogle Scholar
Dobosz, M. (1994 a). New stylus probe with interferometric transducer for surface roughness and form profiling. Optical Engineering 33(3), 902–907.CrossRefGoogle Scholar
Dobosz, M. (1994 b). Application of a divergent laser beam in a grating interferometer for high-resolution displacement measurements. Optical Engineering 33(3), 897–901.CrossRefGoogle Scholar
Dobosz, M., & Ratajczyk, E. (1994). Interference probe head for coordinate measuring machines. Measurement 14(2), 117–123.CrossRefGoogle Scholar
Dobosz, M., & Woźniak, A. (2003). Metrological feasibilities of CMM touch trigger probes: Part II: experimental verification of the 3D theoretical model of probe pretravel. Measurement 34(4), 287–299.CrossRefGoogle Scholar
Estler, W.T., Phillips, S.D., Borchardt, B., Hopp, T., Levenson, M., Eberhardt, K., McClain, M., Shen, Y., & Zhang, X. (1997). Practical aspects of touch-trigger probe error compensation. Precision Engineering 21(1), 1–17.CrossRefGoogle Scholar
Estler, W.T., Phillips, S.D., Borchardt, B., Hopp, T., Witzgall, C., Levenson, M., Eberhardt, K., McClain, M., Shen, Y., & Zhang, X. (1996). Error compensation for CMM touch trigger probes. Precision Engineering 19(2–3), 85–97.CrossRefGoogle Scholar
Goldberg, D.E. (1989). Genetic Algorithms in Search, Optimization and Machine Learning. Boston: Addison–Wesley Longman Publishing Co.Google Scholar
Harary, H., Creimeas, P., Chollet, P., & Clement, A. (1989). A new method for the characterization of analogue probes. IPES, p. 155, Monterey, CA.Google Scholar
ISO. (2001). Geometrical Product Specifications (GPS)—Acceptance and Reverification Tests for Coordinate Measuring Machines (CMM). ISO 10360. Geneva: ISO.Google Scholar
Johnson, R.P., Yang, Q.P., & Butler, C. (1998). Dynamic error characteristics of touch trigger probes fitted to coordinate measuring machines. IEEE Transactions on Instrumentation and Measurement 47(5), 1168–1172.CrossRefGoogle Scholar
Krejci, J.V. (1990). CMM Measurement Enhancement Using Probe Compensation Algorithms. SME Technical Paper MS90-09. Dearborn, MI: Society of Manufacturing Engineers.Google Scholar
Mayer, J.R.R., Ghazzar, A., & Rossy, O. (1996). 3D characterisation, modelling and compensation of the pre-travel of a kinematic touch trigger probe. Measurement 19(2), 83–94.Google Scholar
Miguel, P.C., King, T., & Abackerli, A. (1998). A review on methods for probe performance verification. Measurement 23(1), 15–33.CrossRefGoogle Scholar
Nafi, A., Mayer, J.R.R., & Wozniak, A. (2011). Novel CMM-based implementation of the multi-step method for the separation of machine and probe errors. Precision Engineering 35(2), 318–328.CrossRefGoogle Scholar
Nawara, L., & Sladek, J. (1985). Investigation of measuring heads errors influence on the measuring accuracy of a multicoordinate machine. Proc. Int. Conf. Automated Inspection and Product Control, pp. 305–312.Google Scholar
Reid, C. (1992). Performance characteristics of touch trigger probes. CMM News, NPL, 1.Google Scholar
Shen, Y., & Moon, S. (1997). Mapping of probe pretravel in dimensional measurements using neural networks computational technique. Computers in Industry 34(3), 295–306.CrossRefGoogle Scholar
VDI/VDE. (1989). Accuracy of Coordinate Measuring Machines, Characteristics Parameters and Their Checking, Part 3: Components of Measurement Deviation of the Machine. VDI / VDE 2617. Düsseldorf: Verein Deutcher Ingenieure.Google Scholar
Weckenmann, A., Estler, T., Peggs, G., & McMurtry, D. (2004). Probing systems in dimensional metrology. CIRP Annals, Manufacturing Technology 53(2), 657–684.CrossRefGoogle Scholar
Woźniak, A., & Dobosz, M. (2003). Metrological feasibilities of CMM touch trigger probes. Part I: 3D theoretical model of probe pretravel. Measurement 34(4), 273–286.CrossRefGoogle Scholar
Woźniak, A., & Dobosz, M. (2005). Influence of measured objects parameters on CMM touch trigger probe accuracy of probing. Precision Engineering 29(3), 290–297.CrossRefGoogle Scholar
Yang, Q., Butler, C., & Baird, P. (1996). Error compensation of touch trigger probes. Measurement 18(1), 47–57.CrossRefGoogle Scholar