基于误差分布图的五轴在机测量路径优化方法

Optimization method for five⁃axis on⁃machine measurement path based on error distribution graph

五轴在机测量中,测量路径规划同时决定标定效率、测量效率和机床定位误差影响下的测量精度。为了获得兼顾测量精度与效率的最优测量工艺,提出了一种基于误差分布图的在机测量路径优化方法。首先,根据干涉检查结果构建各待测点的测量可行图。接着,利用定位误差影响模型预测可行图内的测量误差分布。为满足在机测量精度和效率最优,以测量误差作为精度优化目标,结合测量路径长度和标定点数量构建效率优化目标,建立多目标优化模型。基于NSGA-Ⅱ求解模型,优化测量路径。最后,以离心式叶轮为例进行验证,采用此方法不仅能减少红宝石触碰点和测量路径长度以提高效率,同时还能精确补偿预行程误差并减少引入的机床定位误差影响以提升测量精度,实验结果表明该优化方法使标定时间减少55.28%,测量时间减少10.2%,同时平均测量误差减少26.7%,说明该方法具有良好的可行性。

In five-axis on-machine measurement,the measurement path planning determines calibration efficiency,measurement efficiency,and measurement accuracy under the influence of machine positioning errors.In order to obtain a measurement path that balances measurement accuracy and efficiency,this paper proposes an on-machine measurement path optimization method based on the error distribution graph.First,the measurement feasible graph of each to-be-measured point is constructed based on the results of interference checking.Then,a positioning error impact model is used to predict the measurement error distribution within the feasible graph.To improve the on-machine measurement accuracy and efficiency,the measurement error is used as the accuracy optimization target,and the efficiency optimization target is constructed by combining the measurement path length and the number of calibration points,and therefore,the multi-objective optimization model is established.NSGA-Ⅱis used to solve the optimization model and optimize the measurement path.Finally,the centrifugal impeller is used as an example to verify that this method can not only reduce the ruby touch points and measurement path length to improve efficiency,but also accurately compensate for the pre-travel error and reduce the effect of the introduced machine positioning error to improve the measurement accuracy.The experimental results show that the optimized method can reduce the calibration time by 55.28%,the measurement time by 10.2%,and the average measurement error by 26.7%,indicating that the method has good feasibility.