Distortion-Free Intelligent Sampling of Sparse Surfaces Via Locally Refined T-Spline Metamodelling
Distortion-Free Intelligent Sampling of Sparse Surfaces Via Locally Refined T-Spline Metamodelling
复制标题
通过局部细化 T 样条元建模对稀疏曲面进行无失真智能采样
DOI：
10.1007/s40684-020-00248-w
复制
发表时间：
2020-07-27
影响因子：
4.2
通讯作者：
Jiang, Xiangqian Jane
中科院分区：
文献类型：
--
作者：
Wang, Jian;Leach, Richard;Jiang, Xiangqian Jane
在测量不同几何形状的表面时，自动设计采样点和采样位置的数量对于实现自主制造至关重要。均匀采样广泛应用于平面、球面等简单几何形状的测量。然而，缺乏适当的采样技术，可以应用于复杂的自由曲面，特别是那些稀疏的地形特征，如切割边缘和其他高曲率的功能。提出了一种稀疏曲面的无失真智能采样与重构方法。该方法首先采用局部细化T样条逼近，将曲面映射到简化的T样条空间;然后采用平移不变的空间采样方法和相应的重构方法进行曲面测量。这种采样策略提供了一种成本有效的采样设计，并保证在T样条空间中没有信息丢失的曲面重建。理论论证和案例研究表明，这种采样策略可以提供高达一个数量级的提高精度或效率超过国家的最先进的方法，稀疏表面的测量，从宏观到纳米尺度。
Automatic design of the number of sample points and sample locations when measuring surfaces with different geometries is of critical importance to enable autonomous manufacturing. Uniform sampling has been widely used for simple geometry measurement, e.g. planes and spheres. However, there is a lack of appropriate sampling techniques that can be applied to complex freeform surfaces, especially those with sparse topographical features, e.g. cutting edges and other high-curvature features. In this paper, a distortion-free intelligent sampling and reconstruction method with improved efficiency for sparse surfaces is proposed. In this method, a locally-refined T-spline approximation is firstly applied which maps a surface to a simplified T-spline space; then a shift-invariant space sampling method and corresponding reconstruction are applied for the surface measurement. This sampling strategy provides a cost-effective sampling design and guarantees the surface reconstruction without information loss in a T-spline space. Theoretical demonstrations and case studies show that this sampling strategy can provide up to an order of magnitude improvement in accuracy or efficiency over state-of-the-art methods, for the measurement of sparse surfaces, from macro- to nano-scales.