School of Instrument Science and Opto-electric Engineering, Hefei University of Technology, Hefei 230009, China.
Rev Sci Instrum. 2020 Dec 1;91(12):125106. doi: 10.1063/5.0028365.
A new method for further improving the measuring length accuracy of the articulated arm coordinate measuring machine (AACMM) is proposed. The detailed procedure of the proposed method involves kinematic error calibration with the Levenberg-Marquardt algorithm and then non-kinematic error (such as link deflection, thermal errors, and error motions of the rotation shaft) compensation with a back-propagation neural network optimized by the mind evolutionary algorithm. In order to verify the effectiveness and correctness of the proposed method, the simulation and experiment were carried out on an AACMM. The simulated and experimental results demonstrate that the measuring length accuracy of the AACMM is improved significantly after kinematic error calibration and non-kinematic error compensation, confirming the effectiveness and correctness of the proposed method.
提出了一种进一步提高关节臂坐标测量机（AACMM）测量长度精度的新方法。该方法的详细步骤包括使用 Levenberg-Marquardt 算法进行运动学误差校准，然后使用通过思维进化算法优化的反向传播神经网络进行非运动学误差（例如连杆挠度、热误差和旋转轴误差运动）补偿。为了验证所提出方法的有效性和正确性，在 AACMM 上进行了仿真和实验。仿真和实验结果表明，经过运动学误差校准和非运动学误差补偿后，AACMM 的测量长度精度得到了显著提高，证实了所提出方法的有效性和正确性。
