PSI - Issue 52

ScienceDirect Available online at www.sciencedirect.com ScienceDirect Structural Integrity Procedia 00 (2022) 000 – 000 Available online at www.sciencedirect.com ScienceDirect Structural Integrity Procedia 00 (2022) 000 – 000 Available online at www.sciencedirect.com Procedia Structural Integrity 52 (2024) 551–559

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www.elsevier.com/locate/procedia

2452-3216 © 2023 The Authors. Published by ELSEVIER B.V. This is an open access article under the CC BY-NC-ND license (https://creativecommons.org/licenses/by-nc-nd/4.0) Peer-review under responsibility of Professor Ferri Aliabadi 10.1016/j.prostr.2023.12.055 2452-3216 © 2023 The Authors. Published by ELSEVIER B.V. This is an open access article under the CC BY-NC-ND license (https://creativecommons.org/licenses/by-nc-nd/4.0) Peer-review under responsibility of Professor Ferri Aliabadi 2452-3216 © 2023 The Authors. Published by ELSEVIER B.V. This is an open access article under the CC BY-NC-ND license (https://creativecommons.org/licenses/by-nc-nd/4.0) Peer-review under responsibility of Professor Ferri Aliabadi 1. Introduction Thin-walled structures find a wide application in various engineering systems, including land vehicles, aircraft, and seacraft, owing to their advantageous characteristics such as lightweight design, cost-effectiveness, and the availability of robust analysis methods (Vinson 1988). Consequently, it is essential to evaluate their structural integrity in order to detect defects at an early stage and prevent costly and hazardous failures. In recent times, significant * Corresponding author. E-mail address: francesco.cadini@polimi.it * Corresponding author. E-mail address: francesco.cadini@polimi.it © 2023 The Authors. Published by Elsevier B.V. This is an open access article under the CC BY-NC-ND license (https://creativecommons.org/licenses/by-nc-nd/4.0) Peer-review under responsibility of Professor Ferri Aliabadi Abstract Composite plates are increasingly used in several engineering fields. A common way for monitoring the health state of these structures is by analysing ultrasonic guided waves propagating in the plate. Among guided waves, Lamb waves (LWs) have shown promising diagnostic capabilities, and have been recently used for damage diagnosis in deep learning-based frameworks. However, so far, the proposed frameworks have mainly leveraged supervised algorithms, which require acquiring and labelling a large amount of data when the structure is in healthy and damaged conditions. Besides requiring much time and effort, acquiring enough data in damaged structures may not be practical in real world. Hence, this paper proposes the use of conditional generative adversarial networks (CGANs) with convolutional layers for damage localization in composite plates. As unsupervised algorithms, CGANs can be trained on LWs acquired when the structure is healthy, and do not require information about damaged states. The proposed method was validated through an experimental case study involving two different composite plates. Abstract Composite plates are increasingly used in several engineering fields. A common way for monitoring the health state of these structures is by analysing ultrasonic guided waves propagating in the plate. Among guided waves, Lamb waves (LWs) have shown promising diagnostic capabilities, and have been recently used for damage diagnosis in deep learning-based frameworks. However, so far, the proposed frameworks have mainly leveraged supervised algorithms, which require acquiring and labelling a large amount of data when the structure is in healthy and damaged conditions. Besides requiring much time and effort, acquiring enough data in damaged structures may not be practical in real world. Hence, this paper proposes the use of conditional generative adversarial networks (CGANs) with convolutional layers for damage localization in composite plates. As unsupervised algorithms, CGANs can be trained on LWs acquired when the structure is healthy, and do not require information about damaged states. The proposed method was validated through an experimental case study involving two different composite plates. Keywords: Structural Health Monitoring; Lamb Waves; Damage Localization; Conditional Generative Adversarial Networks 1. Introduction Thin-walled structures find a wide application in various engineering systems, including land vehicles, aircraft, and seacraft, owing to their advantageous characteristics such as lightweight design, cost-effectiveness, and the availability of robust analysis methods (Vinson 1988). Consequently, it is essential to evaluate their structural integrity in order to detect defects at an early stage and prevent costly and hazardous failures. In recent times, significant Fracture, Damage and Structural Health Monitoring Unsupervised Damage Localization In Composite Plates Using Lamb Waves And Conditional Generative Adversarial Networks Marc Parziale a , Luca Lomazzi a , Zahra Rastin b , Marco Giglio a , Francesco Cadini a,* a Politecnico di Milano, Department of Mechanical Engineering, Milan 20156, Italy b Independent researcher Fracture, Damage and Structural Health Monitoring Unsupervised Damage Localization In Composite Plates Using Lamb Waves And Conditional Generative Adversarial Networks Marc Parziale a , Luca Lomazzi a , Zahra Rastin b , Marco Giglio a , Francesco Cadini a,* a Politecnico di Milano, Department of Mechanical Engineering, Milan 20156, Italy b Independent researcher Keywords: Structural Health Monitoring; Lamb Waves; Damage Localization; Conditional Generative Adversarial Networks