Location

Snowmass Village, CO

Start Date

1-1-1995 12:00 AM

Description

Ultrasonic inspection of riveted joints carried out by human operator is cumbersome and time consuming. An automated signal classification system would provide better reliability and accuracy in the determination of crack size and orientation. In this paper, we discuss a neural network designed for use in ultrasonic signal classification. The network can give classification results in a short time which makes possible real time ultrasonic inspection. An automated crack sizing system was presented earlier for similar applications [1] and the present paper is an extension of that work. The latest improvement is the use of numerically obtained ultrasonic data to train the neural network classifier (NNC).

Book Title

Review of Progress in Quantitative Nondestructive Evaluation

Volume

14A

Chapter

Chapter 3: Interpretive Signal Processing and Image Analysis

Section

Neural Nets

Pages

779-786

DOI

10.1007/978-1-4615-1987-4_97

Language

en

File Format

application/pdf

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Jan 1st, 12:00 AM

Crack Sizing Using a Neural network Classifier Trained with Data Obtained form Finite Element Models

Snowmass Village, CO

Ultrasonic inspection of riveted joints carried out by human operator is cumbersome and time consuming. An automated signal classification system would provide better reliability and accuracy in the determination of crack size and orientation. In this paper, we discuss a neural network designed for use in ultrasonic signal classification. The network can give classification results in a short time which makes possible real time ultrasonic inspection. An automated crack sizing system was presented earlier for similar applications [1] and the present paper is an extension of that work. The latest improvement is the use of numerically obtained ultrasonic data to train the neural network classifier (NNC).