An effective weld defect classification algorithm has been developed using a large database of simulated defects. Twenty-five shape descriptors used for the classification were studied and an optimal set of nine descriptors with highest discriminative capability was selected using a statistical approach. A multi-layer perceptron (MLP) neural network was trained using shape parameters extracted from the simulated images of weld defects. By testing on 60 unknown simulated defects, the optimised set of nine shape descriptors gave the highest classification accuracy of 100%. Defect classification on 49 real defects from digitised radiographs produced maximum overall classification accuracy of 97.96%.

/pdf/automatic-classification-of-weld-defects-using-simulated-59mi4xpq5n.pdf

Automatic classification of weld defects using simulated data and an MLP neural network

There are numerous techniques used to measure strain. Most are only capable of taking
surface measurements. The penetrating nature of X-rays has been used to measure
deformation, and thus strain, but only with radiographic images. Radioscopic techniques
are faster and do not require film processing, but produce less detailed results than
digitised radiographic images. The research covered by this thesis tested radioscopic
images and showed them to be suitable for strain measurement.
The thesis includes details of the design and capabilities of the radioscopic equipment. Pin
cushion distortion is a common feature of radioscopic images, and an automatic method of
identifying, and correcting for the distortion was implemented. [Continues.]

An investigation into sub-surface strain measurement using X-ray radioscopy

Noise Resilient Image Segmentation and Classification Methods with Applications in Biomedical and Semiconductor Images

This paper describes a novel approach to the design of an automated system for conventional radiographic inspection of cast engineering components. The system incorporates a series of conventional image processing techniques to efficiently detect and analyse defects in the radiographic image. These are combined with an intelligent knowledge based (1KB) system, which uses a series of rules defined by expert inspectors, to interpret and evaluate defects against the quality assurance of the inspection process. A description of the overall design of the system is given, including the various image processing techniques used. The elements of the 1KB system are described, including the knowledge representation scheme, inference mechanism and control structure, and rules for classification and evaluation. Preliminary results are given indicating that the system is capable of meeting the requirements of an automated system for industrial radiographic inspection.

An automated system for the radiographic inspection of castings

Due to the nature of image formation and the resultant image quality the radiographic image is very difficult to interpret which makes the detection of defects inconsistent. In order to improve this situation image processing techniques are applied to the inspection process. This paper describes two new image processing techniques for the automatic detection of defects in industrial radiographic images. This work is part of a project concerned with the development of an automatic radiographic inspection system to provide on-line quality assurance for engineering components.<
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Image processing for automatic defect detection in industrial radiographic images

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Image processing for automatic defect detection in industrial radiographic images