Error-based segmentation of cloud data for direct rapid prototyping

Abstract

This paper proposes an error-based segmentation approach for direct rapid prototyping (RP) of random cloud data. The objective is to fully integrate reverse engineering and RP for rapid product development. By constructing an intermediate point-based curve model (IPCM), a layer-based RP model is directly generated from the cloud data and served as the input to the RP machine for fabrication. In this process, neither a surface model nor an STL file is generated. This is accomplished via three steps. First, the cloud data is adaptively subdivided into a set of regions according to a given subdivision error, and the data in each region is compressed by keeping the feature points (FPs) within the user-defined shape tolerance using a digital image based reduction method. Second, based on the FPs of each region, an IPCM is constructed, and RP layer contours are then directly extracted from the models. Finally, the RP layer contours are faired with a discrete curvature based fairing method and subsequently closed to generate the final layer-based RP model. This RP model can be directly submitted to the RP machine for prototype manufacturing. Two case studies are presented to illustrate the efficacy of the approach.