3차원 스캔 데이터를 활용한 쉘 구조 부품 유사도 측정 방법

초록

To automatically inspect machined components, including automotive and mechanical equip- ment parts, it is essential to identify the specific part. Although vision and AI technologies have advanced and are widely used for part recognition, errors still occur when identifying symmetri- cal or similarly shaped components. Additionally, to assess dimensional and shape deviations of machined parts, the use of 3D point cloud data is necessary. This study proposes a method for recognizing thin-shaped components using 3D point cloud data. Typically, part identification is performed by comparing the similarity between measured data and CAD data. In this approach, distances are compared using spheres, cylinders, and planes generated through linear and planar fitting of the measured data. However, point cloud data often contain significant noise, and the point distribution is not uniform. To address this issue, this study employs a grid-based struc- ture to extract point cloud data in a form consistent with CAD data, which is then used for sim- ilarity evaluation. For symmetrical components, simple distance comparisons fail to distinguish between different parts. To overcome this limitation, the proposed method divides the region and evaluates similarity within each section, enabling accurate differentiation

키워드