PSI - Issue 68

Available online at www.sciencedirect.com Available online at www.sciencedirect.com ScienceDirect Available online at www.sciencedirect.com ScienceDirect Structural Integrity Procedia 00 (2025) 000–000 Structural Integrity Procedia 00 (2025) 000–000

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Procedia Structural Integrity 68 (2025) 1245–1251

European Conference on Fracture 2024 Evaluation of Crack Front Geometry by Strain Field Classification Using Neural Networks Giovanni Chianese a,* , G. P. Pucillo b , D. Leonetti c, d a Institute of Sciences and Technologies for Sustainable Energy and Mobility (STEMS), National Research Council of Italy, via G. Marconi, Naples, Italy Abstract Determining a relation between the crack length and the compliance in compact tension C(T) specimens during experiments is a topic of great interest, which has been addressed in several researches with 2D finite element (FE) analyses assuming a straight crack front. Relevant standards for the evaluation of the fatigue crack growth rate and fracture toughness of metallic materials prescribe limitations on the crack front curvature. This is because different curvatures in the crack front result in different stress strain fields, making the prediction of the crack length a problem depending on the crack front shape too. Additionally, observation of the crack front during tests or operation of mechanical components is challenging. For all these reasons, this work demonstrates a methodology that aims to simultaneously predict the crack front curvature and track the crack growth in numerical simulations of a standard specimen. This objective is addressed by investigating two different strategies. A first approach aimed to leverage local strain measurements; however, analysis of data revealed that this strategy was not feasible because local measurements were not representative of scenarios with different crack front geometries. In a second approach, the framework is setup as a classification problem and it consists of the combined employment of the full field strain measurement on the lateral face of C(T) specimens and of a convolutional neural network that provides the classification. A dataset is generated by means of parametric 3D FE simulations reproducing different crack front curvatures and lengths for a selected geometry. Results indicate that the crack front geometry is correctly predicted with an average accuracy above 90%, and that the normalized crack length is predicted with RMSE of 0.02. © 2025 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 ECF24 organizers Keywords: Fatigue crack growth; crack front aspect ratio; Finite Element Method, NN-based NDE, Crack front, Compact tension specimen European Conference on Fracture 2024 Evaluation of Crack Front Geometry by Strain Field Classification Using Neural Networks Giovanni Chianese a,* , G. P. Pucillo b , D. Leonetti c, d a Institute of Sciences and Technologies for Sustainable Energy and Mobility (STEMS), National Research Council of Italy, via G. Marconi, Naples, Italy b University of Naples Federico II, Department of Industrial Engineering, P.le V. Tecchio 80, 80125 Naples, Italy c Eindhoven University of Technology, Department of the Built Environment, Eindhoven, The Netherlands d EAISI, Eindhoven, The Netherlands Abstract Determining a relation between the crack length and the compliance in compact tension C(T) specimens during experiments is a topic of great interest, which has been addressed in several researches with 2D finite element (FE) analyses assuming a straight crack front. Relevant standards for the evaluation of the fatigue crack growth rate and fracture toughness of metallic materials prescribe limitations on the crack front curvature. This is because different curvatures in the crack front result in different stress strain fields, making the prediction of the crack length a problem depending on the crack front shape too. Additionally, observation of the crack front during tests or operation of mechanical components is challenging. For all these reasons, this work demonstrates a methodology that aims to simultaneously predict the crack front curvature and track the crack growth in numerical simulations of a standard specimen. This objective is addressed by investigating two different strategies. A first approach aimed to leverage local strain measurements; however, analysis of data revealed that this strategy was not feasible because local measurements were not representative of scenarios with different crack front geometries. In a second approach, the framework is setup as a classification problem and it consists of the combined employment of the full field strain measurement on the lateral face of C(T) specimens and of a convolutional neural network that provides the classification. A dataset is generated by means of parametric 3D FE simulations reproducing different crack front curvatures and lengths for a selected geometry. Results indicate that the crack front geometry is correctly predicted with an average accuracy above 90%, and that the normalized crack length is predicted with RMSE of 0.02. © 2025 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 ECF24 organizers Keywords: Fatigue crack growth; crack front aspect ratio; Finite Element Method, NN-based NDE, Crack front, Compact tension specimen © 2025 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 ECF24 organizers b University of Naples Federico II, Department of Industrial Engineering, P.le V. Tecchio 80, 80125 Naples, Italy c Eindhoven University of Technology, Department of the Built Environment, Eindhoven, The Netherlands d EAISI, Eindhoven, The Netherlands

* Corresponding author. E-mail address: giovanni.chianese@stems.cnr.it * Corresponding author. E-mail address: giovanni.chianese@stems.cnr.it

2452-3216 © 2025 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 ECF24 organizers 2452-3216 © 2025 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 ECF24 organizers

2452-3216 © 2025 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 ECF24 organizers 10.1016/j.prostr.2025.06.194