Please use this identifier to cite or link to this item: http://bura.brunel.ac.uk/handle/2438/26905
Title: Zero-Defect Manufacturing and Automated Defect Detection Using Time of Flight Diffraction (TOFD) Images
Authors: Subramaniam, S
Kanfoud, J
Gan, TH
Keywords: signal processing;image processing;automated defect detection;smart manufacturing;time-of-flight diffraction scanning (TOFD);wavelet transform;segmentation
Issue Date: 21-Sep-2022
Publisher: MDPI
Citation: Subramaniam, S., Kanfoud, J. and Gan, T.H. (2022) 'Zero-Defect Manufacturing and Automated Defect Detection Using Time of Flight Diffraction (TOFD) Images', Machines, 10 (10), 839, pp. 1 - 16. doi: 10.3390/machines10100839.
Abstract: Copyright © 2022 by the authors.Ultrasonic time-of-flight diffraction (TOFD) is a non-destructive testing (NDT) technique for weld inspection that has gained popularity in the industry, due to its ability to detect, position, and size defects based on the time difference of the echo signal. Although the TOFD technique provides high-speed data, ultrasonic data interpretation is typically a manual and time-consuming process, thereby necessitating a trained expert. The main aim of this work is to develop a fully automated defect detection and data interpretation approach that enables predictive maintenance using signal and image processing. Through this research, the characterization of weld defects was achieved by identifying the region of interest from A-scan signals, followed by segmentation. The experimental results were compared with samples of known defect size for validation; it was found that this novel method is capable of automatically measuring the defect size with considerable accuracy. It is anticipated that using such a system will significantly increase inspection speed, cost, and safety.
Description: Data Availability Statement: Data available on request due to restrictions eg privacy or ethical.
URI: http://bura.brunel.ac.uk/handle/2438/26905
DOI: http://dx.doi.org/10.3390/machines10100839
Other Identifiers: ORCID iD: Tat-Hean Gan https://orcid.org/0000-0002-5598-8453.
839
Appears in Collections:Brunel Innovation Centre

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