.png){kind=icon}
.png){kind=icon}
Return
Curvature Gradient-estimation Using CT Sinogram and its Application to Reverse Engineering
.png){kind=icon}
OA
AI
DOI：10.1016/j.cad.2022.103240.png)
.png){kind=icon}
.png){kind=icon}
Share.png){kind=icon}
Save
Abstract
En 中文
In industrial manufacturing, reverse engineering, which can be applied to analyze or re-design, is an essential process, whereby scanned data of real objects are used to generate a CAD model. CAD data have various features such as fillets; therefore, robust and versatile reverse engineering is still difficult. Particularly, an adequate segmentation from real scanned data is challenging, and it is a major obstacle for reverse engineering. In this study, we propose an innovative segmentation from CT scanned data, wherein user-friendly segmentation is achieved by directly computing the curvature gradient from a CT sinogram. We used the proposed method to patch a B-spline surface onto each segmented region. The experimental results show that the proposed method offers robust, versatile, and user-friendly reverse engineering that is faster than conventional manual modeling. In addition, as it is supported by X-ray CT simulation, the proposed method can be applied to various surface mesh data without requiring any actual X-ray CT equipment. (c) 2022 The Authors. Published by Elsevier Ltd. This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/).
Keywords:
Reverse engineering
X-ray CT
Sinogram
Curvature gradient
Segmentation
Morse theory
AI Summary
Key information extracted from the uploaded paper, including a brief overview, abstract, background, key highlights, visual analysis, and future outlook.