Automated defect detection for fast evaluation of real inline CT scans. Issue 3 (2nd July 2020)

• Record Type: Journal Article
• Authors: Schlotterbeck, Maxim; Schulte, Lukas; Alkhaldi, Weaam; Krenkel, Martin; Toeppe, Eno; Tschechne, Stephan; Wojek, Christian
• Is Part Of: Nondestructive testing and evaluation. Volume 35:Issue 3(2020)
• Page Start: 266
• Page End: 275
• Publication Date: 2020-07-02
• Subjects: Inline computed tomography -- automated defect detection -- machine learning; Non-destructive testing -- Periodicals; 620.112705
• DOI: 10.1080/10589759.2020.1785446
• Languages: English
• ISSNs: 1058-9759

Abstract

ABSTRACT: In this paper, we present a fully automatic evaluation approach that can be used for fast inline CT scanning. In contrast to classical defect-recognition algorithms, this approach does not require good image quality. Instead, it allows to distinguish CT artifacts from real defects introduced in the production process. To this end, a three-step workflow was developed, in which any deviations from a reference part are detected and subsequently classified and segmented, to allow automatic decision whether the part fulfills given quality requirements. We demonstrate this approach using CT scans of aluminum castings, typically used in the automotive industry. Also, a comparison between two different segmentation algorithms is shown.