Location

Brunswick, ME

Start Date

1-1-1997 12:00 AM

Description

Ultrasonic weld inspections are typically performed manually, which require significant operator expertise and time. Thus, automation of ultrasonic data analysis is an important area of current research in NDE. There is a need for automated data analysis schemes capable of handling imprecise data and providing results in real time. This paper presents a combination of neural networks and fuzzy-logic to automate different aspects of ultrasonic data analysis. Neural networks automate learning, and hence are best used when the relationship between the input space and the output space is highly nonlinear or unknown. The relationship between ultrasonic A-scan signal characteristics and defect class producing the signal is not straight forward. In this work a multi-layer perceptron is used for defect classification. Results form different feature extraction schemes icluding an unique combination of time- and frequency-domain features is presented. Fuzzy-logic automates knowledge representation using a fuzzy rule base. Hence, fuzzy-logic is best applied in situations where a knowledge base exists in the form of IF-THEN rules In this paper fuzzy-logic is applied to accept/reject criteria for weld evaluation. The advantages of using fuzzy-logic over traditional Boolean tree-based algorithms, for this application, are discussed.

Book Title

Review of Progress in Quantitative Nondestructive Evaluation

Volume

16A

Chapter

Chapter 3: Signal Processing and Image Analysis

Section

Signal Processing

Pages

765-772

DOI

10.1007/978-1-4615-5947-4_100

Language

en

File Format

application/pdf

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Jan 1st, 12:00 AM

Neuro-Fuzzy Systems in Ultrasonic Weld Evaluation

Brunswick, ME

Ultrasonic weld inspections are typically performed manually, which require significant operator expertise and time. Thus, automation of ultrasonic data analysis is an important area of current research in NDE. There is a need for automated data analysis schemes capable of handling imprecise data and providing results in real time. This paper presents a combination of neural networks and fuzzy-logic to automate different aspects of ultrasonic data analysis. Neural networks automate learning, and hence are best used when the relationship between the input space and the output space is highly nonlinear or unknown. The relationship between ultrasonic A-scan signal characteristics and defect class producing the signal is not straight forward. In this work a multi-layer perceptron is used for defect classification. Results form different feature extraction schemes icluding an unique combination of time- and frequency-domain features is presented. Fuzzy-logic automates knowledge representation using a fuzzy rule base. Hence, fuzzy-logic is best applied in situations where a knowledge base exists in the form of IF-THEN rules In this paper fuzzy-logic is applied to accept/reject criteria for weld evaluation. The advantages of using fuzzy-logic over traditional Boolean tree-based algorithms, for this application, are discussed.