PSI - Issue 80

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 80 (2026) 327–338

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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 Ferri Aliabadi 10.1016/j.prostr.2026.02.032 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 * Corresponding author. Tel.: +49-6151-16-30030; fax: +49-6151-16-8975. E-mail address: romana.piat@h-da.de © 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 Ferri Aliabadi Abstract Polymer matrix composites embedded with conductive particles are widely utilized for applications that demand stringent control of the effective electrical resistance (or conductivity) of the material. This property is highly sensitive to the particle shape and size distribution within the composite and their percolation threshold. One of the most widely utilized numerical strategies to model this property is the Resistor Network method. However, it is based on many assumptions of the particle shape and inter particle contact which limits its practical applications. In this work, we have proposed a conditional Generative Adversarial Network (cGAN) based modelling strategy that can accurately capture the flow of electrical current through particles which are connected to multiple other particles in the matrix. The cGAN is trained on data generated by finite element simulations that can model the physics of the problem accurately. It is shown that the GAN based model predicts the electrical flow within the composite and hence the effective electrical resistance much more accurately than the Resistor Network model. Abstract Polymer matrix composites embedded with conductive particles are widely utilized for applications that demand stringent control of the effective electrical resistance (or conductivity) of the material. This property is highly sensitive to the particle shape and size distribution within the composite and their percolation threshold. One of the most widely utilized numerical strategies to model this property is the Resistor Network method. However, it is based on many assumptions of the particle shape and inter particle contact which limits its practical applications. In this work, we have proposed a conditional Generative Adversarial Network (cGAN) based modelling strategy that can accurately capture the flow of electrical current through particles which are connected to multiple other particles in the matrix. The cGAN is trained on data generated by finite element simulations that can model the physics of the problem accurately. It is shown that the GAN based model predicts the electrical flow within the composite and hence the effective electrical resistance much more accurately than the Resistor Network model. Keywords: sConducting polymer composites; electrical conductivity; generative AI; surrogate modelling 1. Introduction Conducting polymer composites incorporate electrically conducting particles in a polymer matrix with an objective of making the otherwise insulating material conductive while taking advantage of molding ability of the polymer. The generated composite material can be molded into different shapes and sizes and can have tailored electrical conductivity as described in Yi et al. (2024). The effective electrical conductivity of such composites Fracture, Damage and Structural Health Monitoring Modelling effective electrical resistance in particle reinforced composites using Generative Adversarial Network Vinit Vijay Deshpande a , Alexander Pascal Happ a , Romana Piat a, * a Department of Mathematics and Natural Sciences, University of Applied Sciences, Darmstadt, Germany Fracture, Damage and Structural Health Monitoring Modelling effective electrical resistance in particle reinforced composites using Generative Adversarial Network Vinit Vijay Deshpande a , Alexander Pascal Happ a , Romana Piat a, * a Department of Mathematics and Natural Sciences, University of Applied Sciences, Darmstadt, Germany Keywords: sConducting polymer composites; electrical conductivity; generative AI; surrogate modelling 1. Introduction Conducting polymer composites incorporate electrically conducting particles in a polymer matrix with an objective of making the otherwise insulating material conductive while taking advantage of molding ability of the polymer. The generated composite material can be molded into different shapes and sizes and can have tailored electrical conductivity as described in Yi et al. (2024). The effective electrical conductivity of such composites * Corresponding author. Tel.: +49-6151-16-30030; fax: +49-6151-16-8975. E-mail address: romana.piat@h-da.de