Thickness-Driven Sheet Metal Segmentation of CT-Scanned Body-in-White

Abstract

Large-scale and high-energy X-ray computed tomography (CT) scanners, such as XXL-CT, can fully digitalize car-sized industrial assemblies. For utilizing the data collected using these scanners in digital engineering applications, a part segmentation method is needed. Hence, we propose a segmentation method for a body-in-white primarily composed of sheet metal. The proposed sheet metal segmentation is driven by the sheet thickness, which is estimated from the CT value profile through the sheet structure. The thickness information is assigned to a triangle mesh representing the medial surface of the sheet metal. Then, a simple region-growing algorithm is used to cluster the mesh triangles with comparable sheet thicknesses. Lastly, the resulting clusters are merged manually to obtain the final part segmentation.

Adapting the Segment Anything Model for Volumetric X-ray Data-Sets of Arbitrary Sizes

“…These approaches, however, are not without their limitations. Some methods necessitate manual intervention and corrections [10,11]; others are specifically tailored to predefined component classes [12]. Challenges associated with data quality, particularly in data-sets with a high incidence of artefacts, can significantly hinder the effectiveness of segmentation algorithms.…”