A High-Precision Comprehensive Calibration Method for Linear and Nonlinear Errors in Scanning Probes of Coordinate Measuring Machines

Le résumé fourni par la source

Accurate calibration of scanning probes is essential for ensuring the measurement precision of coordinate measuring machines (CMMs). However, most existing calibration methods rely on linear models, which are inadequate for compensating nonlinear errors inherent in scanning probes. Furthermore, these methods often overlook the form error of the ruby ball, which further limits calibration accuracy. To address these limitations, this paper proposes a high-precision calibration approach that compensates for both linear and nonlinear errors. For linear errors, we incorporate the wear-induced error of the ruby ball into the parameter identification process, resulting in a more comprehensive and accurate linear calibration model. For nonlinear errors, a novel Nonlinear Error Calibration Neural Network (NEC-NN) is proposed. NEC-NN employs the Regional Modulation Module (RMM) to encode probing direction and the Cascaded Multi-Head Attention Module (C-MHAM) to extract deep-level features, enabling the network to capture directiondependent nonlinear errors. Additionally, normal consistency and spherical surface constraints are integrated into the loss function, reducing the training complexity while improving the adaptability of the model. The CMM used in the experiments has a nominal maximum permissible error of 2 + L[mm]/400 μm. After calibrating the scanning probe using the proposed method, the probe’s maximum probing error on a standard sphere decreased to 0.42 μm, while the CMM’s actual maximum permissible error was reduced to 0.6 + L[mm]/400 μm, demonstrating that the proposed method can significantly improve the overall measurement accuracy of the CMM. The proposed approach provides a more reliable and practical solution for the calibration of CMM scanning probes, thereby enhancing the overall reliability and performance of CMM in industrial applications.

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