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

Applied Sciences

Jiangtao Sun¹  Jamil Kanfoud¹  Istvan Szabo¹  Guojin Feng¹  Tat-Hean Gan¹  Cem Selcuk² 

[1] Brunel Innovation Centre, Brunel University London, Uxbridge, London UB8 3PH, UK;TWI Ltd, Granta Park, Great Abington CB21 6AL, UK;

关键词: Non-Destructive Testing;    computed tomography;    automated defect recognition;    quality assurance;    quality control;    powder metallurgy;    additive manufacturing;    X-ray;    net-shape manufacturing;   

DOI  :  10.3390/app7111156

来源: DOAJ

【 摘 要 】

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.

【 授权许可】

Unknown