20.500.12556/DKUM-91914 New approach for automated explanation of material phenomena (AA6082) using artificial neural networks and ChatGPT Artificial intelligence methods, especially artificial neural networks (ANNs), have increasingly been utilized for the mathematical description of physical phenomena in (metallic) material processing. Traditional methods often fall short in explaining the complex, real-world data observed in production. While ANN models, typically functioning as “black boxes”, improve production efficiency, a deeper understanding of the phenomena, akin to that provided by explicit mathematical formulas, could enhance this efficiency further. This article proposes a general framework that leverages ANNs (i.e., Conditional Average Estimator—CAE) to explain predicted results alongside their graphical presentation, marking a significant improvement over previous approaches and those relying on expert assessments. Unlike existing Explainable AI (XAI) methods, the proposed framework mimics the standard scientific methodology, utilizing minimal parameters for the mathematical representation of physical phenomena and their derivatives. Additionally, it analyzes the reliability and accuracy of the predictions using well-known statistical metrics, transitioning from deterministic to probabilistic descriptions for better handling of real-world phenomena. The proposed approach addresses both aleatory and epistemic uncertainties inherent in the data. The concept is demonstrated through the hot extrusion of aluminum alloy 6082, where CAE ANN models and predicts key parameters, and ChatGPT explains the results, enabling researchers and/or engineers to better understand the phenomena and outcomes obtained by ANNs. artificial neural networks automatic explanation hot extrusion aluminum alloy large language models ChatGPT true false true MDPI Angleški jezik Angleški jezik © 2024 by the authors Znanstveno delo 2025-02-27 15:59:02 2025-02-27 15:59:02 2025-02-28 03:11:41 0000-00-00 00:00:00 2024 0 0 str. 1-18 iss. 16 Vol. 14 2024 0000-00-00 Zaloznikova Objavljeno NiDoloceno 2024-06-20 2024-08-08 2024-08-09 669 204131075 10.3390/app14167015 2076-3417 applsci-14-07015-v2.pdf applsci-14-07015-v2.pdf 1 AD057A8B48A02FF69DD3B62406E128FD fcb5b0745924950f32adbad998fcfc40df6a60b99fd7f9fe284a2baf913f5091 145923a0-f51c-11ef-8ea5-00155d000105 https://dk.um.si/Dokument.php?lang=slv&id=188885 https://dx.doi.org/10.3390/app14167015 0 1e29f8d7-f51c-11ef-8ea5-00155d000105 https://dk.um.si/Dokument.php?lang=slv&id=188886 Fakulteta za gradbeništvo, prometno inženirstvo in arhitekturo 0 0 0