Automated defect recognition as a critical element of a three dimensional X-ray computed tomography imaging-based smart non-destructive testing technique in additive manufacturing of near net-shape parts

Istvan Szabo, Jiangtao Sun, Guojin Feng, Jamil Kanfoud, Tat Hean Gan, Cem Selcuk

Research output: Contribution to journalArticle

1 Citation (Scopus)

Abstract

In this paper, a state of the art automated defect recognition (ADR) system is presented that was developed specifically for Non-Destructive Testing (NDT) of powder metallurgy (PM) parts using three dimensional X-ray Computed Tomography (CT) imaging, towards enabling online quality assurance and enhanced integrity confidence. PM parts exhibit typical defects such as microscopic cracks, porosity, and voids, internal to components that without an effective detection system, limit the growth of industrial applications. Compared to typical testing methods (e.g., destructive such as metallography that is based on sampling, cutting, and polishing of parts), CT provides full coverage of defect detection. This paper establishes the importance and advantages of an automated NDT system for the PM industry applications with particular emphasis on image processing procedures for defect recognition. Moreover, the article describes how to establish a reference library based on real 3D X-ray CT images of net-shape parts. The paper follows the development of the ADR system from processing 2D image slices of a measured 3D X-ray image to processing the complete 3D X-ray image as a whole. The introduced technique is successfully integrated into an automated in-line quality control system highly sought by major industry sectors in Oil and Gas, Automotive, and Aerospace.

Original languageEnglish
Article number1156
Number of pages14
JournalApplied Sciences (Switzerland)
Volume7
Issue number11
DOIs
Publication statusPublished - 10 Nov 2017
Externally publishedYes

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3D printers
Nondestructive examination
Tomography
Powder metallurgy
manufacturing
tomography
powder metallurgy
Imaging techniques
X rays
Defects
defects
Image processing
x rays
image processing
industries
Metallography
Polishing
Quality assurance
metallography
Industrial applications

Cite this

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title = "Automated defect recognition as a critical element of a three dimensional X-ray computed tomography imaging-based smart non-destructive testing technique in additive manufacturing of near net-shape parts",
abstract = "In this paper, a state of the art automated defect recognition (ADR) system is presented that was developed specifically for Non-Destructive Testing (NDT) of powder metallurgy (PM) parts using three dimensional X-ray Computed Tomography (CT) imaging, towards enabling online quality assurance and enhanced integrity confidence. PM parts exhibit typical defects such as microscopic cracks, porosity, and voids, internal to components that without an effective detection system, limit the growth of industrial applications. Compared to typical testing methods (e.g., destructive such as metallography that is based on sampling, cutting, and polishing of parts), CT provides full coverage of defect detection. This paper establishes the importance and advantages of an automated NDT system for the PM industry applications with particular emphasis on image processing procedures for defect recognition. Moreover, the article describes how to establish a reference library based on real 3D X-ray CT images of net-shape parts. The paper follows the development of the ADR system from processing 2D image slices of a measured 3D X-ray image to processing the complete 3D X-ray image as a whole. The introduced technique is successfully integrated into an automated in-line quality control system highly sought by major industry sectors in Oil and Gas, Automotive, and Aerospace.",
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Automated defect recognition as a critical element of a three dimensional X-ray computed tomography imaging-based smart non-destructive testing technique in additive manufacturing of near net-shape parts. / Szabo, Istvan; Sun, Jiangtao; Feng, Guojin; Kanfoud, Jamil; Gan, Tat Hean; Selcuk, Cem.

In: Applied Sciences (Switzerland), Vol. 7, No. 11, 1156, 10.11.2017.

Research output: Contribution to journalArticle

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