Defect detection and classification system for automatic analysis of digital radiography images of PM parts

Abstract

Digital radiography is a promising non-destructive testing tool for powder metallurgy (PM) parts, in which transmitted X-rays are recorded to generate data for an advanced defect detection system. An important part of this system is the data processing platform for pattern recognition in X-ray images. Combinations of advanced techniques for noise reduction, contrast enhancement and image segmentation are employed. Algorithms of registration for images in regions of interest are discussed, e.g. the scale invariant feature transform (SIFT). Modern pattern recognition methodologies such as smoothing, moment representation, image alignment and optical flow towards feature classification are evaluated. The proposed defect detection and classification capability for automatic analysis of digital radiographic images from PM parts potentially allows integration into multiple-view inspection systems, which should enhance quality control in the PM manufacturing and production environment. Defect detection systems able to work at the speed of current production lines are of great interest to both PM manufacturers and users.

Acknowledgements

The research leading to these results has received funding from the European Union's Seventh Framework Programme managed by REA Research Executive Agency http://ec.europa.eu/rea/ (FP7/2007-2013) under grant agreement no 283288. The AutoInspect project is a collaboration between the following organisations: Brunel Innovation Centre of Brunel University, Accent Pro 2000 srl, MIMTech ALFA SL, Polkom Badania, InnotecUK, TWI Ltd, and Vienna University of Technology (www.autoinspectproject.eu). This paper is based on a presentation at Euro PM 2013, organised by EPMA in Gothenburg, Sweden on 15–18 September 2013.