Regulating complex geometries using layered depth-normal images for rapid prototyping and manufacturing

Purpose - Most layer-based rapid prototyping systems use polygonal models as input. In addition, the input polygonal models need to be manifold and water-tight; otherwise the built objects may have defects or the building process may fail in some cases. This paper aims to present a regulation method of an arbitrarily complex polygonal model for rapid prototyping and manufacturing applications. Design/methodology/approach - The method is based on a semi-implicit representation of a solid model named the layered depth-normal images (LDNI), which sparsely encodes the shape boundary of a polygonal model in three orthogonal directions. In the method, input polygonal models or parametric equations are first converted into LDNI models. A regulation operator based on the computed LDNI models is presented. A volume tiling technique is developed for very complex geometries and high accuracy requirements. From the processed LDNI model, an adaptive contouring method is presented to construct a cell representation that includes both uniform and octree cells. Finally, two-manifold and water-tight polygonal mesh surfaces are constructed from the cell representation. Findings - The LDNI-based mesh regulation operation can be robust due to its simplicity. The accuracy of the generated regulated models can be controlled by setting LDNI pixel width. Parallel computing techniques can be employed to accelerate the computation in the LDNI-based method. Experimental results on various CAD models demonstrate the effectiveness and efficiency of our approach for complex geometries. Research limitations/implications The input polygonal model is assumed to be closed in our method. The regulated polygonal model based on our method may have a big file size. Originality/value - A novel mesh regulation method is presented in this paper. The method is suitable for rapid prototyping and manufacturing applications by achieving a balance between simplicity, robustness, accuracy, speed and scalability. This research contributes to the additive manufacturing development by providing a digital data preparation method and related tools.