Adaptive slicing of cloud data for reverse engineering and direct rapid prototyping model construction

In reverse engineering, conventional surface modelling from point cloud data is time-consuming and requires expert modelling skills. One of the innovative modelling methods is to directly slice the point cloud along a direction and generate a layer-based model, which can be used directly for fabrication using rapid prototyping (RP) techniques. However, the main challenge is that the thickness of each layer must be carefully controlled so that each layer will yield the same shape error, which is within the given tolerance bound. In this thesis, an adaptive slicing approach for modelling point cloud data is presented. It seeks to generate a direct RP model with minimum number of layers based on a given shape error. Two methods for adaptive slicing are developed. One uses a correlation coefficient to determine the neighbourhood size of projected data points, so that a polygon can be constructed to approximate the profile of projected data points. The thickness of each layer is iteratively determined by the control of shape error between projected data points and this constructed polygon. The other method has similar steps for slicing and projecting the cloud data as method one, but it uses wavelets to construct a polygon from different levels, and it do better to curve with small features such as sharp corners than method one. Issues including multiple loop segmentation in layers, profile curve generation, and data filtering, are discussed. The efficacy of the algorithms is demonstrated by case studies.

Author dc:creator

• WU YIFENG

• reverse engineering, rapid prototyping, wavelets, cloud data, layer-based model, and shape-error