Unsupervised machine learning for flaw detection in automated ultrasonic testing of carbon fibre reinforced plastic composites.

Autor: Tunukovic V; Sensor Enabled Automation, Robotics, and Control Hub (SEARCH), Centre for Ultrasonic Engineering (CUE), Electronic and Electrical Engineering Department, University of Strathclyde, Glasgow, UK; Future Ultrasonic Engineering, FUSE CDT, Glasgow, UK. Electronic address: vedran.tunukovic@strath.ac.uk., McKnight S; Sensor Enabled Automation, Robotics, and Control Hub (SEARCH), Centre for Ultrasonic Engineering (CUE), Electronic and Electrical Engineering Department, University of Strathclyde, Glasgow, UK., Pyle R; Sensor Enabled Automation, Robotics, and Control Hub (SEARCH), Centre for Ultrasonic Engineering (CUE), Electronic and Electrical Engineering Department, University of Strathclyde, Glasgow, UK., Wang Z; University of Strathclyde, Glasgow, UK., Mohseni E; Sensor Enabled Automation, Robotics, and Control Hub (SEARCH), Centre for Ultrasonic Engineering (CUE), Electronic and Electrical Engineering Department, University of Strathclyde, Glasgow, UK., Gareth Pierce S; Sensor Enabled Automation, Robotics, and Control Hub (SEARCH), Centre for Ultrasonic Engineering (CUE), Electronic and Electrical Engineering Department, University of Strathclyde, Glasgow, UK., K W Vithanage R; Sensor Enabled Automation, Robotics, and Control Hub (SEARCH), Centre for Ultrasonic Engineering (CUE), Electronic and Electrical Engineering Department, University of Strathclyde, Glasgow, UK., Dobie G; Sensor Enabled Automation, Robotics, and Control Hub (SEARCH), Centre for Ultrasonic Engineering (CUE), Electronic and Electrical Engineering Department, University of Strathclyde, Glasgow, UK., MacLeod CN; Sensor Enabled Automation, Robotics, and Control Hub (SEARCH), Centre for Ultrasonic Engineering (CUE), Electronic and Electrical Engineering Department, University of Strathclyde, Glasgow, UK., Cochran S; Future Ultrasonic Engineering, FUSE CDT, Glasgow, UK., O'Hare T; Spirit AeroSystems, Belfast, UK.
Jazyk: angličtina
Zdroj: Ultrasonics [Ultrasonics] 2024 May; Vol. 140, pp. 107313. Date of Electronic Publication: 2024 Apr 06.
DOI: 10.1016/j.ultras.2024.107313
Abstrakt: The use of Carbon Fibre Reinforced Plastic (CFRP) composite materials for critical components has significantly surged within the energy and aerospace industry. With this rapid increase in deployment, reliable post-manufacturing Non-Destructive Evaluation (NDE) is critical for verifying the mechanical integrity of manufactured components. To this end, an automated Ultrasonic Testing (UT) NDE process delivered by an industrial manipulator was developed, greatly increasing the measurement speed, repeatability, and locational precision, while increasing the throughput of data generated by the selected NDE modality. Data interpretation of UT signals presents a current bottleneck, as it is still predominantly performed manually in industrial settings. To reduce the interpretation time and minimise human error, this paper presents a two-stage automated NDE evaluation pipeline consisting of a) an intelligent gating process and b) an autoencoder (AE) defect detector. Both stages are based on an unsupervised method, leveraging density-based spatial clustering of applications with noise clustering method for robust automated gating and undefective UT data for the training of the AE architecture. The AE network trained on ultrasonic B-scan data was tested for performance on a set of reference CFRP samples with embedded and manufactured defects. The developed model is rapid during inference, processing over 2000 ultrasonic B-scans in 1.26 s with the area under the receiver operating characteristic curve of 0.922 in simple and 0.879 in complex geometry samples. The benefits and shortcomings of the presented methods are discussed, and uncertainties associated with the reported results are evaluated.
Competing Interests: Declaration of competing interest The authors declare the following financial interests/personal relationships which may be considered as potential competing interests: [Gareth Pierce reports financial support was provided by Spirit AeroSystems Inc. If there are other authors, they declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.].
(Copyright © 2024 The Author(s). Published by Elsevier B.V. All rights reserved.)
Databáze: MEDLINE
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