Multipoint flatness assessment method based on support vector regression

Abstract

The invention discloses a multipoint flatness assessment method based on support vector regression, wherein the method comprises the following steps of: sampling points on the surface of a measured flat plate to acquire a three-coordinate measured value of each point; pre-processing the original measured points, measuring a three-dimensional convex housing of a point set by calculating to remove measured points in the convex housing and keep the measuring points on the convex housing; using a support vector regression method-epsilon-SVR (Support Vector Regression) to acquire a least contain area plane of the measuring point set, wherein the corresponding contain area width is the acquired flatness. With the method, the calculation data quantity in the multipoint flatness assessment can bereduced, the calculating efficiency and the degree of accuracy of the assessment result can be improved.

Description

technical field [0001] The invention relates to a flatness evaluation method, in particular to a multi-measurement-point flatness evaluation method based on support vector regression. Background technique [0002] With the development of the national economy and the improvement of advanced manufacturing levels, in modern industrial manufacturing, especially in the fields of precision manufacturing, there are high precision requirements for the geometrical and dimensional accuracy of workpieces and structures. As one of the most common geometric dimensions of industrial products, planes are often used as pressure-bearing planes and reference planes in practical applications. If the flatness of the workpiece exceeds the design requirements, it will lead to uneven stress on the pressure bearing surface, which will easily cause local deformation or damage of the pressure bearing surface, or cause the geometric dimensions of the components based on the plane to be inaccurate, and...

AI Technical Summary

Problems solved by technology

The calculation speed of the least square method is faster, but it is difficult to guarantee the evaluation accuracy by using the least square plane instead of the minimum area plane; the calculation accuracy of the search method is high, but it is easy to fall into a local optimum in the iterative search; the calculation accuracy of the geometric method is High, but it is necessary to obtain the minimum area plane through all enumerations, and the calculation is complicated

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