Uncertainty-guided intelligent sampling strategy for high-efficiency surface measurement via free-knot B-spline regression modelling

Jing Wang, Luca Pagani, Liping Zhou, Xiaojun Liu, Wenlong Lu, Richard Leach, Xiangqian Jiang

Research output: Contribution to journalArticle

5 Citations (Scopus)


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.
Original languageEnglish
Pages (from-to)38-52
Number of pages15
JournalPrecision Engineering
Early online date8 Sep 2018
Publication statusPublished - 1 Mar 2019


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