Improved surface extraction of multi-material components for single-source industrial X-ray computed tomography

Abstract

The paper demonstrates a novel methodology for surface extraction of multi-material components (MMCs) on industrial X-ray computed tomography (CT) datasets. The methodology is based on a combination of fuzzy C-means clustering (FCM) and region growing (RG) methods. FCM, used as a pre-processing step, allows proper classification and improvement of different objects boundaries present on industrial X-ray CT datasets. Afterwards, application of RG method enables accurate segmentation of classified and improved X-ray CT datasets. The performance of presented approach has been tested on two CT datasets acquired on an industrial X-ray CT system NIKON XT H 225. It was also compared against two commercial industrial software VGStudio Max v3.1 and GOM Inspect v2018. Obtained results from application of the proposed approach show significant improvement in surface extraction of MMCs in CT datasets, especially in cases of low-density materials such as polymers. Verification has been conducted by obtaining reference measurements using contact coordinate measuring machine (CMM) Contura G2 by CARL ZEISS.