Machine Learning for Nondestructive Evaluation
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 function-finding 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 knowledge-intensive classification processes.
Cite
Text
O'Rorke et al. "Machine Learning for Nondestructive Evaluation." International Conference on Machine Learning, 1991. doi:10.1016/B978-1-55860-200-7.50126-4Markdown
[O'Rorke et al. "Machine Learning for Nondestructive Evaluation." International Conference on Machine Learning, 1991.](https://mlanthology.org/icml/1991/oaposrorke1991icml-machine/) doi:10.1016/B978-1-55860-200-7.50126-4BibTeX
@inproceedings{oaposrorke1991icml-machine,
title = {{Machine Learning for Nondestructive Evaluation}},
author = {O'Rorke, Paul and Morris, Steven and Amirfathi, Michael and Bond, William E. and Clair, Daniel C. St.},
booktitle = {International Conference on Machine Learning},
year = {1991},
pages = {620-624},
doi = {10.1016/B978-1-55860-200-7.50126-4},
url = {https://mlanthology.org/icml/1991/oaposrorke1991icml-machine/}
}