Intelligent sampling can be used to influence the efficiency of surface geometry measurement. With no design model information provided, reconstruction from prior sample points with a surrogate model has to be carried out iteratively, thus the next best sample point(s) can be intelligently selected. But, a lack of accurate and fast reconstruction models hinders the development of intelligent sampling techniques. In this paper, a smart surrogate model based on free-knot B-splines is used for intelligent surface sampling design with the aid of uncertainty modelling. By implementing intelligent sampling in a Cartesian, parametric or specific error space, the proposed method can be flexibly applied to reverse engineering and geometrical tolerance inspection, especially for high-dynamic-range structured surfaces with sparse and sharply edged features. Extensive numerical experiments on simulated and real surface data are presented. The results show that this parametric model-based method can achieve the same or higher sampling efficiency as some recent non-parametric methods but with far less computing time cost.
|Number of pages||15|
|Early online date||8 Sep 2018|
|Publication status||Published - 1 Mar 2019|
Wang, J., Pagani, L., Zhou, L., Liu, X., Lu, W., Leach, R., & Jiang, X. (2019). Uncertainty-guided intelligent sampling strategy for high-efficiency surface measurement via free-knot B-spline regression modelling. Precision Engineering, 56, 38-52. https://doi.org/10.1016/j.precisioneng.2018.09.002