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

4 Citations (Scopus)

Abstract

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.
LanguageEnglish
Pages38-52
Number of pages15
JournalPrecision Engineering
Volume56
Early online date8 Sep 2018
DOIs
Publication statusPublished - 1 Mar 2019

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Surface measurement
Splines
Sampling
Reverse engineering
Inspection
Uncertainty
Geometry
Costs
Experiments

Cite this

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title = "Uncertainty-guided intelligent sampling strategy for high-efficiency surface measurement via free-knot B-spline regression modelling",
abstract = "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.",
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Uncertainty-guided intelligent sampling strategy for high-efficiency surface measurement via free-knot B-spline regression modelling. / Wang, Jing; Pagani, Luca; Zhou, Liping; Liu, Xiaojun; Lu, Wenlong; Leach, Richard; Jiang, Xiangqian.

In: Precision Engineering, Vol. 56, 01.03.2019, p. 38-52.

Research output: Contribution to journalArticle

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AU - Leach, Richard

AU - Jiang, Xiangqian

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