Share Email Print
cover

Proceedings Paper

Hole detection on aluminum plates using inductive learning
Author(s): Thomas D. Snyder; Peter M. Tappert; Harry H. Robertshaw
Format Member Price Non-Member Price
PDF $14.40 $18.00

Paper Abstract

This work discusses the effects of inherent variabilities on the damage identification problem and the creation of a practical damage identification method. Variability is present any time there are factors which have the potential to change during the course of the damage identification process. There are many variabilities which are inherent in damage identification and can cause problems when attempting to detect damage. Manufacturing variability is one of these variabilities and is shown experimentally to be a `non-qualifiable' one. Inductive learning is a tool which has been proposed to be an effective method of performing damage identification. This method is modified to accommodate manufacturing variability and shown to successfully detect hole damage on aluminum plates.

Paper Details

Date Published: 8 May 1995
PDF: 6 pages
Proc. SPIE 2443, Smart Structures and Materials 1995: Smart Structures and Integrated Systems, (8 May 1995); doi: 10.1117/12.208268
Show Author Affiliations
Thomas D. Snyder, Virginia Polytechnic Institute and State Univ. (United States)
Peter M. Tappert, Virginia Polytechnic Institute and State Univ. (United States)
Harry H. Robertshaw, Virginia Polytechnic Institute and State Univ. (United States)


Published in SPIE Proceedings Vol. 2443:
Smart Structures and Materials 1995: Smart Structures and Integrated Systems
Inderjit Chopra, Editor(s)

© SPIE. Terms of Use
Back to Top