X-ray inspection utilizing knowledge-based feature isolation with a neural network classifier

Adam R. Nolan; Yong-Lin Hu; William G. Wee

doi:10.1117/12.46480

1 August 1991 X-ray inspection utilizing knowledge-based feature isolation with a neural network classifier

Adam R. Nolan, Yong-Lin Hu, William G. Wee

Proceedings Volume 1472, Image Understanding and the Man-Machine Interface III; (1991) https://doi.org/10.1117/12.46480
Event: Orlando '91, 1991, Orlando, FL, United States

Abstract

This paper describes a generalized flaw detection scheme for a molded and machined turbine blade. The data used are radiograph images. Based on knowledge of the molding and machining process, selective features may be isolated and classified for each possible flaw candidate. The proposed classification system requires the incorporation of many smaller pattern recognition systems. Several of these pattern recognition subsystems have been developed and implemented. Described is the implementation of one such subsystem whose characteristics are best realize utilizing a back propagation neural network. The results of the network are compared with other classification schemes (K nearest neighbor and Bayes classifier).

Citation Download Citation

Adam R. Nolan, Yong-Lin Hu, and William G. Wee "X-ray inspection utilizing knowledge-based feature isolation with a neural network classifier", Proc. SPIE 1472, Image Understanding and the Man-Machine Interface III, (1 August 1991); https://doi.org/10.1117/12.46480

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