Pattern Recognition for Nondestructive Evaluation

The issues involved in automating nondestructive evaluation (NDE) techniques are outlined. Attention is given to research focused on the application of machine learning techniques to the construction and maintenance of knowledge-based systems which are capable of evaluating the readings from nondestructive tests that have been performed on aircraft components. Preliminary results obtained from this research are described. In particular, the authors discuss the application of a symbolic machine learning algorithm, ID3, to the NDE problem. ID3 has been used by Douglas Aircraft to classify defects in sets of standard NDE reference blocks. Based on the preliminary results, a need for an improved method of distinguishing features in the test waveforms is identified. The authors also outline a feature extraction approach from pattern recognition, called scale-space filtering, which can be used to preprocess data for input into a classification algorithm such as ID3

Pattern recognition for nondestructive evaluation

This paper outlines the issues involved in automating nondestructive evaluation techniques. Nondestructive evaluation techniques are used to inspect a variety of parts during manufacturing and service. Currently, humans analyze the output obtained from test techniques by looking for features which indicate that a defect is located in the material. This evaluation is dependent on both the experience and alertness of the technician performing the test. Automation of these processes should improve the consistency of results and enhance the testing of more complex materials. Machine learning and pattern recognition techniques are being investigated to automate the process