Intelligent Search-Based Selection of Sample Points for Straightness and Flatness Estimation

摘要

Form error estimation with a CMM requires prudent sampling and accurate zone fitting. This paper proposes use of optimization search methods for reducing sample size, while maintaining high accuracy. The approach is demonstrated with examples of straightness and flatness. For straightness, region-elimination search is used. For flatness, two pattern search methods: Tabu search and a hybrid search, are employed and their performance is compared. Sampling begins with a necessary number of initial points and a zone is determined. Next points are sampled based on the search methods, with suitable application of intensification/diversification strategies, looking for improvement in the zone fit. Search is conducted in both the +ve and -ve directions from the fit feature and is stopped when a solution for the maximum deviation is realized. The two solution points are added to the initial set and the corresponding tolerance is computed. The tolerance is compared with that obtained for the population of a large sample, to verify the accuracy. It is found that the number of points sampled is potentially less than that typically used to achieve the same accuracy.