Unsupervised classification of ultrasonic nondestructive testing (NDT) data

Ilkka Ylaekoski

doi:10.1117/12.189085

13 October 1994 Unsupervised classification of ultrasonic nondestructive testing (NDT) data

Ilkka Ylaekoski

Proceedings Volume 2354, Intelligent Robots and Computer Vision XIII: 3D Vision, Product Inspection, and Active Vision; (1994) https://doi.org/10.1117/12.189085
Event: Photonics for Industrial Applications, 1994, Boston, MA, United States

Abstract

Ultrasonic non-destructive testing is used both in manufacturing and in maintenance to ensure quality. In ultrasonic testing, a scanning probe transmits ultrasound pulses and the signal scattered back is detected by a receiver. The time of flight information is often called the A- scan. The A-scans form two dimensional images (B, C, D-scans) corresponding to different projection planes. The scanning over a surface provides information about both the location and the size of the defects and produces huge data files. Therefore, A-scans are often reduced to a single C-scan. In this set-up, the probe angle is zero degrees and the inspection plane is near the focus plane. The inspector uses C-scans or also A and B-scans, if they are available, in defect assessment. An approach to combine the reduction of memory space and the categorization of defects is proposed. Each A-scan is clustered in an unsupervised manner into a number of classes using a self-organizing feature map. The self-organizing process produces a feature map where similar A-scans are close to one another. The classes are visualized by assigning a color for each neurone, so that similar A-scans will get similar colors. In the classified C-scan, different defects can be easily distinguished by their color. The proposed method supersedes the typical C-scan methods by its ability to classify the defects using the characteristic features of the A-scans.

Citation Download Citation

Ilkka Ylaekoski "Unsupervised classification of ultrasonic nondestructive testing (NDT) data", Proc. SPIE 2354, Intelligent Robots and Computer Vision XIII: 3D Vision, Product Inspection, and Active Vision, (13 October 1994); https://doi.org/10.1117/12.189085

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