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Statistical Sampling Strategies for Geometric Tolerance Inspection by CMM

Author

Listed:
  • Colosimo Bianca Maria

    (Dipartimento di Meccanica, Politecnico di Milano, Via la Masa 1, 20156 Milano, Italy. e-mail: biancamaria.colosimo@polimi.it)

  • Moya Ester Gutierrez

    (Dep. Industrial Management, University of Seville, c/Camino de los Descubrimientos s/n, 41092 Sevilla, Spain. e-mail: egm@esi.us.es)

  • Moroni Giovanni

    (Dipartimento di Meccanica, Politecnico di Milano, Via la Masa 1, 20156 Milano, Italy. e-mail: giovanni.moroni@mecc.polimi.it)

  • Petrò Stefano

    (Dipartimento di Meccanica, Politecnico di Milano, Via la Masa 1, 20156 Milano, Italy. e-mail: stefano.petro@polimi.it)

Abstract

More and more often, procedures for profile/surface monitoring and inspection refer to geometric specifications. In fact, the number of form tolerance requirements (such as straightness, roundness, flatness, cylindricity, free-form profiles and surfaces) are increasingly appearing in technical drawings. Usually, Coordinate Measuring Machines (CMMs) are used to measure points on the specific feature under study in order to decide whether the machined item is conforming to requirements. In fact, measured points are used to compute the form error which has to be smaller than the tolerance value to avoid scrapping or reworking. The estimated form error is usually affected by uncertainty, which depends on both the sample size (number of points measured on the profile/surface) and the sampling strategy (location of the points on the profile/surface). The present research work focuses on the second issue and investigates the performance of several existing sampling strategies with the ones achieved by two newly presented approaches. These new approaches explore advantages arising by including a statistical analysis of a set of machined features in the sampling strategy design. Throughout the paper, a real case study concerning flatness tolerance is used as reference.

Suggested Citation

  • Colosimo Bianca Maria & Moya Ester Gutierrez & Moroni Giovanni & Petrò Stefano, 2008. "Statistical Sampling Strategies for Geometric Tolerance Inspection by CMM," Stochastics and Quality Control, De Gruyter, vol. 23(1), pages 109-121, January.
  • Handle: RePEc:bpj:ecqcon:v:23:y:2008:i:1:p:109-121:n:1
    DOI: 10.1515/EQC.2008.109
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    References listed on IDEAS

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    1. Cadima, Jorge & Cerdeira, J. Orestes & Minhoto, Manuel, 2004. "Computational aspects of algorithms for variable selection in the context of principal components," Computational Statistics & Data Analysis, Elsevier, vol. 47(2), pages 225-236, September.
    2. I. T. Jolliffe, 1972. "Discarding Variables in a Principal Component Analysis. I: Artificial Data," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 21(2), pages 160-173, June.
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