Adaptive sampling method for thin-walled parts based on on-machine measurement

Machining deformation compensation technology based on on-machine measurement has been widely used in the field of thin-walled part machining. However, few research has been conducted on sampling methods for the measurement of thin-walled parts. In this study, we considered the influence of machining deformation in thin-walled regions, established a machining deformation prediction model (MDPM) based on the finite element method (FEM), and applied it to the sampling optimization process. Furthermore, we proposed an adaptive sampling method based on the maximum corresponding point deviation (MCPD) at the measurement point interval of the non-uniform rational B-spline (NURBS) curve. The proposed method was compared with three commonly used sampling methods (uniform sampling, curvature-based sampling, and maximum deviation-based sampling). Sampling experiments were performed with one NURBS curve and two machined thin-walled parts. The experimental results show that the proposed method is superior to the three commonly used sampling strategies in terms of reconstruction accuracy, sampling efficiency, and result stability.