Location

Williamsburg, VA

Start Date

1-1-1986 12:00 AM

Description

There are a number of modern approaches that can be used to characterize flaws in materials. For example, one method, which has been described recently by Wormley and Thompson [1], uses a model-based approach to obtain the “best fit” size and orientation parameters based on a simple equivalent shape such as ellipsoid. Before such sizing estimates can be activated, however, it is first necessary to determine if the unknown flaw being examined is a volumetric or crack-like flaw, since the sizing algorithm will be different for each case. This classification problem, although it is conceptually simpler than the more complete problem of flaw characterization, is, nevertheless, a difficult challenge because of the large number of parameters that can influence the resulting signals. A summary of our recent work on the flaw classification problem is given below. As will be shown, we have chosen to use a combination of signal processing, modeling and artificial intelligence tools to try to pare down the complexity of the ultrasonic responses and isolate those features that are dependent only on flaw-type.

Book Title

Review of Progress in Quantitative Nondestructive Evaluation

Volume

5A

Chapter

Chapter 3: Sensors and Signal Processing

Section

Signal Processing

Pages

755-758

DOI

10.1007/978-1-4615-7763-8_79

Language

en

File Format

application/pdf

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Jan 1st, 12:00 AM

New Approaches to Ultrasonic Flaw Classification Using Signal Processing, Modeling, and Artificial Intelligence Concepts

Williamsburg, VA

There are a number of modern approaches that can be used to characterize flaws in materials. For example, one method, which has been described recently by Wormley and Thompson [1], uses a model-based approach to obtain the “best fit” size and orientation parameters based on a simple equivalent shape such as ellipsoid. Before such sizing estimates can be activated, however, it is first necessary to determine if the unknown flaw being examined is a volumetric or crack-like flaw, since the sizing algorithm will be different for each case. This classification problem, although it is conceptually simpler than the more complete problem of flaw characterization, is, nevertheless, a difficult challenge because of the large number of parameters that can influence the resulting signals. A summary of our recent work on the flaw classification problem is given below. As will be shown, we have chosen to use a combination of signal processing, modeling and artificial intelligence tools to try to pare down the complexity of the ultrasonic responses and isolate those features that are dependent only on flaw-type.