Complex surface self-adaptive sampling method

Abstract

The invention discloses a complex surface self-adaptive sampling method. The content of the method comprises the steps that a sampling quantity is determined according to sampling precision; complex surface projective points are included into a given square region and converted into two-dimensional projective points with density properties; sample points are randomly generated under set initial temperature; search is performed on the sample points, and a mean value of two-generation generators is calculated and made to approach a centroid point step by step; a variance function is used as a criterion to determine the goodness/badness of solutions, and for deteriorative solutions, a random probability is set to expand a solution space; the generators are updated, and the coincident precision of the generators and the centroid point is judged; the generator with the highest coincident precision is selected to serve as the centroid point and a globally optimal solution, and return is performed; and the optimal solution is mapped back to a complex surface to serve as an optimal sampling solution for output. The method is high in calculation speed and efficiency, and measurement efficiency and measurement precision of surface self-adaptive sampling are improved.

Description

technical field [0001] The invention relates to an adaptive sampling method for curved surfaces, in particular to an adaptive sampling method for complex curved surfaces. The method is an adaptive sampling method based on the combination of MacQueen K-means algorithm and simulated annealing algorithm (MKSA). Background technique [0002] Complex surface parts are widely used in high-end manufacturing equipment fields such as ships, precision molds, aerospace, and national defense. Contact part inspections based on three-coordinate measuring machines and machine tool measurements need to specify limited sampling measurement points for measurement to detect processing quality. Surface adaptive sampling can effectively control the scale of collected measurement data on the basis of ensuring measurement accuracy, improve measurement efficiency, thereby reducing enterprise measurement costs and improving enterprise competitiveness. Therefore, the study of surface adaptive sampling...

AI Technical Summary

Problems solved by technology

[0007] In order to solve the deficiencies in the existing surface adaptive sampling method and further improve the fitting surface measurement accuracy and measurement efficiency of surface adaptive sampling, the present invention provides a complex surface adaptive sampling method, which is based on the MacQueen K-means algorithm Adaptive sampling method combined with simulated annealing algorithm (MKSA)

The method of the invention is mainly used to solve the self-adaptive sampling problem of complex curved surfaces that cannot be composed of elementary analytical curved surfaces

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