Abstract
Among the engineering applications of Intelligent Systems, the field of Non-Destructive Testing and Evaluation (NDT/NDE) appears to be particularly interesting for its industrial relevance. NDT plays an increasingly important role in diverse areas of modern society ranging from aerospace and nuclear power plants to oil and gas transportation. In addition, NDT techniques are of relevant interest in subsurface mine detection, seismic engineering and geophysical applications. The goal of most NDT techniques is to reveal anomalies in a test object without destroying or somehow perturb the test specimen. Various physical principles, ranging from acoustics, thermal, optics and mechanics to nuclear physics, have been proposed and utilized in NDT/NDE techniques. Measurements are affected by various sources of inaccuracies, namely, the imprecise contour and shape of the defect, the geometry of the inspected specimens, the measurement noise, and the imprecise location of sensors and probes. Intelligent processing techniques can be useful in various respects. The decision about the presence of the defect is a difficult task that involves prior knowledge on the problem in terms of human expertise. Computationally expensive optimization and iteration procedures are used to accurately extract any important information from raw data. Some information is basically a context property. Models based on Artificial Neural Networks (ANNs), fuzzy systems, approximate reasoning, and information decision trees can be of help in solving this problem. In this chapter, a critical analysis of novel computing techniques for treating the characterization problem (determining number, size, and location of defects in specimens) is carried out. Several examples of intelligent processing of NDT/NDE data will be proposed throughout this chapter.
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Morabito, F.C. (2000). Intelligent Non-Destructive Testing and Evaluation with Industrial Applications. In: Teodorescu, HN., Mlynek, D., Kandel, A., Zimmermann, HJ. (eds) Intelligent Systems and Interfaces. International Series in Intelligent Technologies, vol 15. Springer, Boston, MA. https://doi.org/10.1007/978-1-4615-4401-2_12
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