Analysis of Spherical Form Errors to Coordinate Measuring Machine Data

Abstract

Coordinates measuring machines (CMMs) are commonly utilized to take measurement data from manufactured surfaces for inspection purposes. The measurement data are then used to evaluate the geometric form errors associated with the surface. Traditionally, the evaluation of spherical form errors involves an optimization process of fitting a substitute sphere to the sampled points. This paper proposes the computational strategies for sphericity with respect to ASME Y14.5M-1994 standard. The proposed methods consider the trade-off between the accuracy of sphericity and the efficiency of inspection. Two approaches of computational metrology based on genetic algorithms (GAs) are proposed to explore the optimality of sphericity measurements and the sphericity feasibility analysis, respectively. The proposed algorithms are verified by using several CMM data sets. Observing from the computational results, the proposed algorithms are practical for on-line implementation to the sphericity evaluation. Using the GA-based computational techniques, the accuracy of sphericity assessment and the efficiency of sphericity feasibility analysis are agreeable.