基于蒙特卡洛的同轴度误差检测测点优化

Optimization of Measuring Points for Coaxiality Error Detection Based on Monte Carlo

针对当前零件同轴度误差检测测点数偏多问题,提出一种基于蒙特卡洛法的同轴度误差检测测点优化方法。在给定零件加工方法下,通过分析零件误差来源预测零件尺寸误差;根据误差来源和同轴度公差工程语义构建零件表面形状模拟函数;利用表面形状模拟函数构建测点集模拟函数,通过同轴度误差评定方法分析给定零件测量截面数与误差评定值的关系,并进一步基于蒙特卡洛法分析批量零件的最佳测量截面数。最后,以某阀芯为例,通过模拟仿真分析该零件最佳测量截面数,并以三坐标测量机验证得到该零件最佳测量截面数。实例结果表明,采用所提出的测点优化方法使单个截面上测点数减少983个,截面数减少246个,能有效减少测点数,提高测量效率。

Aiming at the problem that there are too many measuring points for coaxiality error detection of parts, an optimization method for coaxiality error detection measuring points based on Monte Carlo method is proposed.Under a given part processing method, predict the dimensional error of the part by analyzing the source of the part error;construct the part surface shape simulation function according to the error source and coaxiality tolerance engineering semantics;use the surface shape simulation function to construct the measurement point set simulation function, and pass the coaxial The degree error evaluation method analyzes the relationship between the number of measurement sections of a given part and the error evaluation value, and further analyzes the optimal number of measurement sections of a batch of parts based on the Monte Carlo method.Finally, taking a spool as an example, the best measurement section number of the part is analyzed through simulation, and the best measurement section number of the part is verified by a coordinate measuring machine.The example results show that the proposed method of measuring point optimization reduces the number of measuring points on a single cross-section by 983 and the number of cross-sections by 246,which can effectively reduce the number of measuring points and improve measurement efficiency.