A Support-Vector-Machine Method for Precisely Evaluating Planar Straightness Error

摘要

To present a precise and efficient algorithm to solve planar straightness error problems, a machine-learning approach to evaluate planar straightness error was presented in this paper. According to the similarity between the least envelope zone and the support vector regression model, the SVR-based method was developed to solve the problem of straightness error. The evaluation method was compared to some existing techniques. According to the results from three datasets, it is shown that the SVR-based method can provide precise and exact values of planar straightness error.