Machine Learning For Nondestructive Evaluation

Author

Paul O'Rorke
Steven Morris
Michael Amirfathi
William E. Bond, Missouri University of Science and TechnologyFollow
Daniel C. St Clair, Missouri Univeristy of Science and Technology

Abstract

This paper reports on the current status of a collaborative project exploring applications of machine learning methods to Nondestructive Evaluation (NDE). It presents initial results of applying AI methods for inductive learning, feature extraction, and functionfinding to support the ultrasonic diagnosis of defective metal parts. Experience with a simple classification approach using ID3 has led us to develop an adaptation of a machine vision technique to obtain a more abstract feature representation. Learning from examples expressed in the new representation is expected to be less sensitive to noise. In addition, the new representation better supports function-finding and more knowledgeintensive classification processes.

Recommended Citation

P. O'Rorke et al., "Machine Learning For Nondestructive Evaluation," Proceedings of the 8th International Workshop on Machine Learning, ICML 1991, pp. 620 - 624, Elsevier, Jan 1991.

The definitive version is available at https://doi.org/10.1016/B978-1-55860-200-7.50126-4

Department(s)

Computer Science

Second Department

Mathematics and Statistics

Comments

National Science Foundation, Grant IRI-8813048

International Standard Book Number (ISBN)

978-155860200-7

Document Type

Article - Conference proceedings

Document Version

Citation

File Type

text

Language(s)

English

Rights

© 2023 Elsevier, All rights reserved.

Publication Date

01 Jan 1991