TY - JOUR
T1 - Uncertainty-guided intelligent sampling strategy for high-efficiency surface measurement via free-knot B-spline regression modelling
AU - Wang, Jing
AU - Pagani, Luca
AU - Zhou, Liping
AU - Liu, Xiaojun
AU - Lu, Wenlong
AU - Leach, Richard
AU - Jiang, Xiangqian
PY - 2019/3/1
Y1 - 2019/3/1
N2 - 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.
AB - 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.
KW - Adaptive sampling
KW - Free-knot B-Splines
KW - Surface reconstruction
KW - Uncertainty analysis
KW - Structured surfaces
UR - http://www.scopus.com/inward/record.url?scp=85060980109&partnerID=8YFLogxK
U2 - 10.1016/j.precisioneng.2018.09.002
DO - 10.1016/j.precisioneng.2018.09.002
M3 - Article
VL - 56
SP - 38
EP - 52
JO - Precision Engineering
JF - Precision Engineering
SN - 0141-6359
ER -