Chen Hongfang, Wu Huan, Gao Yi, Shi Zhaoyao, Wen Zhongpu, Liang Ziqi
Beijing Engineering Research Center of Precision Measurement Technology and Instruments, Beijing University of Technology, Beijing 100124, China.
Rev Sci Instrum. 2024 Aug 1;95(8). doi: 10.1063/5.0206876.
A particle swarm algorithm-based identification method for the optimal measurement area of large coordinate measuring machines (CMMs) is proposed in this study to realize the intelligent identification of measurement objects and optimize the measurement position and measurement space using laser tracer multi-station technology. The volumetric error distribution of the planned measurement points in the CMM measurement space is obtained using laser tracer multi-station measurement technology. The volumetric error of the specified step distance measurement points is obtained using the inverse distance weighting (IDW) interpolation algorithm. The quasi-rigid body model of the CMM is solved using the LASSO algorithm to obtain the geometric error of the measurement points in a specified step. A model of individual geometric errors is fitted with least squares. An error optimization model for the measurement points in the CMM space is established. The particle swarm optimization algorithm is employed to optimize the model, and the optimal measurement area of the CMM airspace is determined. The experimental results indicate that, when the measurement space is optimized based on the volume of the object being measured, with dimensions of (35 × 35 × 35) mm3, the optimal measurement area for the CMM, as identified by the particle swarm algorithm, lies within the range of 150 mm < X < 500 mm, 350 mm < Y < 700 mm, and -430 mm < Z < -220 mm. In particular, the optimal measurement area is defined as 280 mm < X < 315 mm, 540 mm < Y < 575 mm, and -400 mm < Z < -365 mm. Comparative experiments utilizing a high-precision standard sphere with a diameter of 19.0049 mm and a sphericity of 50 nm demonstrate that the identified optimal measurement area is consistent with the results obtained through the particle swarm algorithm, thereby validating the correctness of the method proposed in this study.
本研究提出一种基于粒子群算法的大型坐标测量机（CMM）最优测量区域识别方法，以利用激光跟踪仪多站技术实现测量对象的智能识别，并优化测量位置和测量空间。采用激光跟踪仪多站测量技术获取CMM测量空间中规划测量点的体积误差分布。利用反距离加权（IDW）插值算法获得指定步距测量点的体积误差。使用LASSO算法求解CMM的准刚体模型，以获得指定步长下测量点的几何误差。用最小二乘法拟合单个几何误差模型。建立CMM空间中测量点的误差优化模型。采用粒子群优化算法对模型进行优化，确定CMM空域的最优测量区域。实验结果表明，当根据被测物体的体积对测量空间进行优化时，对于尺寸为(35×35×35)mm³的物体，粒子群算法识别出的CMM最优测量区域在150 mm < X < 500 mm、350 mm < Y < 700 mm和 -430 mm < Z < -220 mm范围内。特别地，最优测量区域定义为280 mm < X < 315 mm、540 mm < Y < 575 mm和 -400 mm < Z < -365 mm。利用直径为19.0049 mm、球度为50 nm的高精度标准球进行的对比实验表明，识别出的最优测量区域与通过粒子群算法获得的结果一致，从而验证了本研究提出方法的正确性。
