【摘要】 针对某立式加工中心的制造一致性问题,采用非参数统计中的Kruskal-Wallis检验,分析了影响Y轴运动直线度的因素,提出了基于支持向量回归机(Support Vector Regression,SVR)的Y轴运动直线度精度区间预测算法,通过遗传算法对支持向量回归机的惩罚函数参数C和高斯核函数参数gamma进行了优化,使算法具有了更高的预测精度和更好的适应性;在对精度区间准确预测的基础上,通过KM(Kuhn-Munkras)算法对机床底座和装配人员进行二分匹配,显著提高了Y轴运动直线度的一致性。结果表明:采用支持向量回归机预测算法在置信度为90%的情况下其预测的精度区间宽度为3μm,蒙特卡洛模拟显示一致性提升了47%,可为提高立式加工中心制造一致性提供新思路。更多还原

【Abstract】 For the manufacturing consistency problem of a vertical machining center,the Kruskal-Wallis test in non-parametric statistics was used to analyze the factors affecting the Y-axis motion straightness. A prediction algorithm of the Y-axis motion straightness based on the Support Vector Regression(SVR) was proposed. The genetic algorithm optimized C and gamma in Gaussian kernel parameters of SVR,which made the algorithm has higher prediction accuracy and better adaptability.On the basis of accurate prediction of accuracy interval,the KM(Kuhn-Munkras) algorithm was applied to the bipartite graph matching for the base of the machine tool and the assembler,significantly improved the consistency of the Y-axis motion straightness. The results show that the SVR prediction algorithm has an interval width of 3 μm at a confidence level of 90 %,and Monte Carlo simulation displayed a 47 % improvement in consistency,which provided new ideas for improving the consistency of manufacturing of vertical machining centers.更多还原