To address the issue of varying influence weights of geometric errors in actual five-axis machining, this paper proposes a sensitivity analysis method that combines the projection relationship of individual errors onto the composite error vector with the cumulative error of each axis. Partial differential equations establish error projection formulas for individual geometric errors in each direction. Based on the actual motion trajectories of each axis during the machining of the S-shaped specimen, cumulative error calculations are performed within each axis’s travel range. Following normalization processing, sensitivity coefficients for individual errors are obtained, successfully identifying 15 critical errors. After compensating for the identified critical errors through simulation, the surface accuracy of the S-shaped specimen machined by the machine tool improved by 63.24% compared to pre-compensation. The results demonstrate that this method achieves high efficiency and effectiveness in identifying critical geometric errors of five-axis machine tools, providing a new approach for enhancing machine tool precision
Zhang et al. (Mon,) studied this question.