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RISS 인기검색어
- 검색키워드
- 저자 : Kazuaki Kawashima (검색결과 3 건)
- 무료
- 기관 내 무료
- 유료
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Kawashima, Kazuaki,Kanai, Satoshi,Date, Hiroaki Society for Computational Design and Engineering 2014 Journal of computational design and engineering Vol.1 No.1
Recently, renovations of plant equipment have been more frequent because of the shortened lifespans of the products, and as-built models from large-scale laser-scanned data is expected to streamline rebuilding processes. However, the laser-scanned data of an existing plant has an enormous amount of points, captures intricate objects, and includes a high noise level, so the manual reconstruction of a 3D model is very time-consuming and costly. Among plant equipment, piping systems account for the greatest proportion. Therefore, the purpose of this research was to propose an algorithm which could automatically recognize a piping system from the terrestrial laser-scanned data of plant equipment. The straight portion of pipes, connecting parts, and connection relationship of the piping system can be recognized in this algorithm. Normal-based region growing and cylinder surface fitting can extract all possible locations of pipes, including straight pipes, elbows, and junctions. Tracing the axes of a piping system enables the recognition of the positions of these elements and their connection relationship. Using only point clouds, the recognition algorithm can be performed in a fully automatic way. The algorithm was applied to large-scale scanned data of an oil rig and a chemical plant. Recognition rates of about 86%, 88%, and 71% were achieved straight pipes, elbows, and junctions, respectively.
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Kazuaki Kawashima,Satoshi Kanai (사)한국CDE학회 2010 한국CAD/CAM학회 국제학술발표 논문집 Vol.2010 No.8
The performance of the three-dimensional non-contact measuring devises (3D laser or industrial X-ray CT scanning devices) have been rapidly developed, and the importance of reverse engineering(RE) increases where 3D CAD model is reconstructed from scanned mesh model measured from real objects. However, existing reverse engineering methods cannot recover the feature-based model representations that might be defined in CAD systems, therefore the reconstructed CAD models cannot be easily faired and modified for the effective use in downstream applications. The purpose of the research is to propose a new algorithm that can robustly recognize 2.5 dimensional features and can detect the defining parameters of them from the scanned meshes including scanning error and noise. Among the features which are popularly used for feature-based solid modeling in commercial CAD systems, revolved features can be recognized in our algorithm. The defining parameters of a revolved feature consist of a rotational axis, a sketch defining plane, a 2D sketch profile and a rotational angle. Firstly, curvature and principle directions are evaluated from vertices of scanned mesh, then, the boundary surface of the scanned mesh is segmented into regions. Then, rotational axes of the regions are estimated from gauss images of maximum principle direction vectors. Same rotational axes are then integrated to the one axis. Then, a sketch defining plane is determined as a plane including the rotational axis, and the vertices are projected to the plane, then, lines and arcs which minimize distance between vertices and the lines or the arcs are fitted to the projected vertices with dynamic programming and Hough transform in order to obtain a 2D sketch profile. Then, the position and orientation of the rotational axis and the 2D sketch profile shape are modified with constraint fitting. Lastly, the defining parameters of the features are transmitted to a commercial CAD system via API functions, and a final feature-based solid model is created in the system. A prototype system was developed. And the algorithm was tested for a scanned mesh obtained from a X-ray CT scanning device and the dimensional accuracy of the defining parameters were verified. Dimension errors of the 2D sketch profile were less than 0.2mm. Given the average edge length of the scanned mesh(1.06mm), the algorithm could recognize revolved features and reconstruct a 3D CAD model in high accuracy.
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Kazuaki Kawashima,Satoshi Kanai,Hiroaki Date (사)한국CDE학회 2013 한국CAD/CAM학회 국제학술발표 논문집 Vol.2010 No.8
Recently, renovations of plant equipment have been more frequent because of the shorten lifetimes of the products, and as-built models from large-scale laser scanned data is expected to streamline their rebuilding processes. However, the laser scanned data of the existing plant has enormous number of points, captures intricate objects and includes high level of noises, so that the manual reconstruction of a 3D model is very time-consuming and costs a lot. Among plant equipment, piping systems especially account for the greatest proportion of plant equipment. Therefore, the purpose of this research was to propose an algorithm which can automatically recognize a piping system from terrestrial laser scanned data of the plant equipment. The straight portion of pipes, connecting parts and connection relationship of the piping system can be recognized in this algorithm. Normal-based region-growing and cylinder surface fitting can extract all candidates of points of pipes including straight pipes, elbows and junctions. Tracing axes of piping system allows to recognize the positions of these elements and their connection relationship. Using only point clouds, the recognition algorithm can be performed in a fully automatic way. The algorithm was applied to large-scale scanned data of an oil rig. The results of the recognition rate of straight pipes, elbows, junctions were achieved at 93%, 92% and 91% respectively
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