Decomposing Scanned Assembly Meshes Based on Periodicity Recognition and Its Application to Kinematic Simulation Modeling

Along with the recent growth of industrial X-ray computerized tomography (CT) scanning systems, it is now possible to non-destructively acquire the entire meshes of assemblies. This technology has the potential to realize an advanced inspection process of an assembly, such as estimation of their assembly errors or examinations of their dynamic behaviors in motion using a model reflecting real assembled situations. However, to realize the process, it is necessary to accurately decompose themesh and to extract a set of partial meshes, each of which corresponds to a single part, from the entire meshes of assemblies measured from the CT scans.Moreover, it is required to createmodels that are ready for dynamic behavior simulations. In this paper, we focus on CT scanned meshes of gear assemblies as examples, and propose beneficial methods for establishing such advanced inspections.We first propose amethod that accurately decomposes the mesh into partial meshes, each of which corresponds to a single gear, using periodicity recognitions. The key idea is first to accurately recognize the periodicity of each gear, then to extract sets of topologically connected mesh elements where periodicities are valid, and finally to interpolate points in plausible ways from an engineering viewpoint to the area where surface meshes are not generated, especially the contact area betweenparts in theCT scanningprocess.Wealso propose amethod for creating kinematic simulation models which can be used for a gear teeth contact evaluation using extracted partial meshes and their periodicities. Such an evaluation of teeth contacts is one of the most important functions in kinematic simulations of gear assemblies for predicting the power transmission efficiency, noise and vibration. The characteristics of the proposed method is that (1) it can robustly and accurately recognize periodicities fromnoisy scannedmeshes, (2) it can estimate the plausible boundaries of neighboring parts without any previous knowledge from single-material CT scannedmeshes, and (3) it can efficiently extract partial meshes from large scanned meshes containing millions of triangles in a few minutes. We demonstrate the effectiveness of our method on a variety of artificial and real CT scanned meshes. © 2011 Elsevier Ltd. All rights reserved.