Artificial Intelligence and Image Processing Approaches in Damage Assessment and Material Evaluation

The ultrasonic is an inspection technique (UT), which employs high frequency acoustic waves to probe the sample being inspected. As the acoustic wave penetrates the sample, the wave is attenuated and/or reflected as a result of variation in the density (sound velocity) of the material. By observing and post processing the returned signal, be it the reflected signal or the signal emanating from the opposite side of the sample, one can effectively evaluate the material´s characteristics such as material microstructures, as well as flaws existing in the material. This paper describes different artificial intelligence (AI) and image processing methods, which could be utilized to investigate various defects in metals as well as composites. The proposed system is highly robust and effective in situations where a large number of similar samples are to be investigated. The proposed methods utilizes artificial neural networks (ANN), fuzzy logic and image analysis to recognize various types of defects in a given specimen. Image processing and wavelets techniques are used to determine the details of the damage geometry. The above system is an integral part of a robust damage analysis software under the development. An adaptive neuro fuzzy inference system is also being developed for composites, suggestive repair mechanism. MATLAB language is used in developing a real time automated damage assessment and evaluation prototype system