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- W2891727175 abstract "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." @default.
- W2891727175 created "2018-09-27" @default.
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- W2891727175 date "2019-03-01" @default.
- W2891727175 modified "2023-10-16" @default.
- W2891727175 title "Uncertainty-guided intelligent sampling strategy for high-efficiency surface measurement via free-knot B-spline regression modelling" @default.
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- W2891727175 doi "https://doi.org/10.1016/j.precisioneng.2018.09.002" @default.
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