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|Title:||An adaptive slicing approach to modelling cloud data for rapid prototyping||Authors:||Zhang, Y.F.
|Issue Date:||22-Sep-2003||Citation:||Zhang, Y.F., Wong, Y.S., Loh, H.T., Wu, Y.F. (2003-09-22). An adaptive slicing approach to modelling cloud data for rapid prototyping. Journal of Materials Processing Technology 140 (1-3 SPEC.) : 105-109. ScholarBank@NUS Repository. https://doi.org/10.1016/S0924-0136(03)00824-0||Abstract:||In this paper, an adaptive slicing method for modelling point cloud data is presented. A layer-based rapid prototyping (RP) model is directly constructed from arbitrarily scattered cloud data and fed to RP machines for fabrication. The emphasis is on how to control the thickness of each layer so that a user-specified shape error is met. Given a set of unorganised point cloud acquired by scanning a part, the first step is to segment the cloud data into a number of layers by slicing the point cloud along the part building direction (user-specified). Taking the first layer, the data points are projected onto a plane. These projected data points are then compressed and sorted to keep only the key feature points (FPs) using a reduction method based on linear correlation. The FPs are then used to construct a polygon approximation of the points. The maximum deviation of the points in the layer is then compared to the given shape error to decide whether to reduce or increase the layer thickness. This process is repeated until the maximum deviation is just below the given shape error and the first layer thus obtained. Subsequently, the remaining layers are obtained in the same manner. The algorithm has been implemented using VC++ and OpenGL. Testing results are very much satisfactory. Compared with traditional modelling methods, this method is highly automated and the generated layer-based model is highly efficient for RP manufacturing. © 2003 Elsevier B.V. All rights reserved.||Source Title:||Journal of Materials Processing Technology||URI:||http://scholarbank.nus.edu.sg/handle/10635/73145||ISSN:||09240136||DOI:||10.1016/S0924-0136(03)00824-0|
|Appears in Collections:||Staff Publications|
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