Three-dimensional ultrasonic self supervised segmentation

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McKnight, Shaun and Tunukovic, Vedran and Hifi, Amine and Pierce, S. Gareth and Mohseni, Ehsan and MacLeod, Charles N. and O’Hare, Tom (2025) Three-dimensional ultrasonic self supervised segmentation. Engineering Applications of Artificial Intelligence, 154. 110870. ISSN 0952-1976 (https://doi.org/10.1016/j.engappai.2025.110870)

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Abstract

This study introduces a novel self-supervised learning approach for volumetric segmentation of defect indications captured by phased array ultrasonic testing data from Carbon Fiber Reinforced Polymers. By employing this self-supervised method, defect segmentation is achieved automatically without the need for labelled training data or examples of defects. The approach has been tested using artificially induced defects, including back-drilled holes and Polytetrafluoroethylene inserts, to mimic different defect responses. Additionally, it has been evaluated on stepped geometries with varying thickness, demonstrating impressive generalization across various test scenarios. Minimal preprocessing requirements are needed, with no removal of geometric features or Time-Compensated Gain necessary for applying the methodology. The model's performance was evaluated for defect detection, in-plane and through-thickness localisation, as well as defect sizing. All defects were consistently detected with thresholding and different processing steps able to remove false positive indications for a 100 % detection accuracy. Defect sizing aligns with the industrial standard 6 dB amplitude drop method, with a Mean Absolute Error (MAE) of 1.41 mm (mm). In-plane and through-thickness localisation yielded comparable results, with MAEs of 0.37 and 0.26 mm, respectively. Visualisations are provided to illustrate how this approach can be utilised to generate digital twins of components.

ORCID iDs

McKnight, Shaun ORCID logoORCID: https://orcid.org/0000-0002-3904-5092, Tunukovic, Vedran ORCID logoORCID: https://orcid.org/0000-0002-3102-9098, Hifi, Amine, Pierce, S. Gareth ORCID logoORCID: https://orcid.org/0000-0003-0312-8766, Mohseni, Ehsan ORCID logoORCID: https://orcid.org/0000-0002-0819-6592, MacLeod, Charles N. ORCID logoORCID: https://orcid.org/0000-0003-4364-9769 and O’Hare, Tom;
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