PSI - Issue 38

Available online at www.sciencedirect.com Structural Int grity Procedia 00 (2021) 000 – 000 Available online at www.sciencedirect.com ScienceDirect Structural Integrity Procedia 00 (2021) 000 – 000 Available online at www.sciencedirect.com ScienceDirect

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Procedia Structural Integrity 38 (2022) 168–181

© 2021 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 the scientific committee of the Fatigue Design 2021 Organizers © 2021 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 the scientific committee of the Fatigue Design 2021 Organizers Keywords: Machine learning; finite element modelling; crack identification; aircraft structure; structure optimization; data mining; big data; digitalization 1. Introduction During their lifespan, aircraft structures are subject to significant loadings that generate, on the long-term, fatigue damage [1]. In order to obtain a certification that guarantees the safety of the aircraft, a comprehensive set of tests (e.g. fatigue testing) have to be performed. Whole ranges of flight conditions are repeatedly simulated in testing facilities, primarily Abstract A software architecture based on Machine Learning (ML) nd Finite Element Method (FEM) and aimed at improving the detection of damages in aircraft structure subjected to complex variable loadings is presented here. Firstly, the software relies on statistical tools used among others in fraud detection (One-Class Support Vector Machine, Local Outlier Factors, Isolation Forest, DBSCAN) to identify anomalies in a vast amount of dat rec rded over time by multiple strain gauges located on the structure of the aircraft. Once an anomaly is detected at a given time and for a specific set of strain g uges, it can be classified as insignificant or critical by the user. If the anomaly is critical, the data of the ass ciate strain gauges can e used as input data for a FEM optimization. This static optimization allows to visually assess the position and geometry of possible cracks in the structure. © 2021 The Authors. Published by ELSEVIER B.V. This is an open access article under t CC BY-NC-ND lic nse (https://creativecommons.org/licenses/by-nc-nd/4.0) Peer-review under responsibility of the scientific committee of the Fatigue Design 2021 Organizers Keywords: Machine learning; finite element modelling; crack identification; aircraft structure; structure optimization; data mining; big data; digitalization 1. Introduction During their lifespan, aircraft structures are subject to significant loadings that generate, on the long-term, fatigue damage [1]. In order to obtain a certification that guarantees the safety of the aircraft, a co prehensive set of tests (e.g. fatigue testing) have to be performed. Whole ranges of flight conditions are repeatedly simulated in testing facilities, primarily FATIGUE DESIGN 2021, 9th Edition of the International Conference on Fatigue Design How fraud detection technologies can help to detect damages in aircraft structures A. Cugniere a * , O. Tusch a , A. Mösenbacher a a IABG mbH, TAM3 Strength, Computation & Method Development, Einsteinstr. 20, 85521 Ottobrunn, Bavaria, Germany Abstract A software architecture based on Machine Learning (ML) and Finite Element Method (FEM) and aimed at improving the detection of damages in aircraft structure subjected to complex variable loadings is presented here. Firstly, the software relies on statistical tools used among others in fraud detection (One-Class Support Vector Machine, Local Outlier Factors, Isolation Forest, DBSCAN) to identify anomalies in a vast amount of data recorded over time by multiple strain gauges located on the structure of the aircraft. Once an anomaly is detected at a given time and for a specific set of strain gauges, it can be classified as insignificant or critical by the user. If the anomaly is critical, the data of the associated strain gauges can be used as input data for a FEM optimization. This static optimization allows to visually assess the position and geometry of possible cracks in the structure. FATIGUE DESIGN 2021, 9th Edition of the International Conference on Fatigue Design How fraud detection technologies can help to detect damages in aircraft structures A. Cugniere a * , O. Tusch a , A. Mösenbacher a a IABG mbH, TAM3 Strength, Computation & Method Development, Einsteinstr. 20, 85521 Ottobrunn, Bavaria, Germany

* E-mail address: cugniere@iabg.de * E-mail address: cugniere@iabg.de

2452-3216 © 2021 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 the scientific committee of the Fatigue Design 2021 Organizers 2452-3216 © 2021 The Authors. Published by ELSEVIER B.V. This is an ope acces article under CC BY-NC-ND lic nse (https://cr ativecommons.org/l c nses/by-nc-nd/4.0) Peer-review under responsibility of the scientific committee of the Fatigue Design 2021 Organizers

2452-3216 © 2021 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 the scientific committee of the Fatigue Design 2021 Organizers 10.1016/j.prostr.2022.03.018