Hole detection on aluminum plates using inductive learning

Thomas D. Snyder; Peter M. Tappert; Harry H. Robertshaw

doi:10.1117/12.208268

8 May 1995 Hole detection on aluminum plates using inductive learning

Thomas D. Snyder, Peter M. Tappert, Harry H. Robertshaw

Proceedings Volume 2443, Smart Structures and Materials 1995: Smart Structures and Integrated Systems; (1995) https://doi.org/10.1117/12.208268
Event: Smart Structures and Materials '95, 1995, San Diego, CA, United States

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.

Citation Download Citation

Thomas D. Snyder, Peter M. Tappert, and Harry H. Robertshaw "Hole detection on aluminum plates using inductive learning", Proc. SPIE 2443, Smart Structures and Materials 1995: Smart Structures and Integrated Systems, (8 May 1995); https://doi.org/10.1117/12.208268

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