Flaws Identification Using Eddy Current Differential Transducer and Artificial Neural Networks
- Szczecin University of Technology. al Piastow 17, 70-310 Szczecin (Poland)
In this paper we present a multi-frequency excitation eddy current differential transducer and dynamic neural models which were used to detect and identify artificial flaws in thin conducting plates. Plates are made of Inconel600. EDM notches have relative depth from 10% to 80% and length from 2 mm to 7 mm. All flaws were located on the opposite surface of the examined specimen. Measured signals were used as input for training and verifying dynamic neural networks with a moving window. Wide range of ANN (Artificial Neural Network) structures are examined for different window length and different number of frequency components in excitation signal. Observed trends are presented in this paper.
- OSTI ID:
- 20798265
- Journal Information:
- AIP Conference Proceedings, Journal Name: AIP Conference Proceedings Journal Issue: 1 Vol. 820; ISSN APCPCS; ISSN 0094-243X
- Country of Publication:
- United States
- Language:
- English
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