| Title: | New approach for automated explanation of material phenomena (AA6082) using artificial neural networks and ChatGPT |
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| Authors: | ID Goričan, Tomaž (Author)
ID Terčelj, Milan (Author)
ID Peruš, Iztok (Author) |
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| Files: |  applsci-14-07015-v2.pdf (3,18 MB)
MD5: AD057A8B48A02FF69DD3B62406E128FD
 https://dx.doi.org/10.3390/app14167015
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| Language: | English |
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| Work type: | Scientific work |
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| Typology: | 1.01 - Original Scientific Article |
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| Organization: | FGPA - Faculty of Civil Engineering, Transportation Engineering and Architecture
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| Abstract: | 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. |
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| Keywords: | artificial neural networks, automatic explanation, hot extrusion, aluminum alloy, large language models, ChatGPT |
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| Publication status: | Published |
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| Publication version: | Version of Record |
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| Submitted for review: | 20.06.2024 |
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| Article acceptance date: | 08.08.2024 |
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| Publication date: | 09.08.2024 |
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| Publisher: | MDPI |
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| Year of publishing: | 2024 |
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| Number of pages: | str. 1-18 |
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| Numbering: | Vol. 14, iss. 16 |
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| PID: | 20.500.12556/DKUM-91914  |
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| UDC: | 669 |
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| ISSN on article: | 2076-3417 |
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| COBISS.SI-ID: | 204131075 |
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| DOI: | 10.3390/app14167015 |
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| Copyright: | © 2024 by the authors |
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| Publication date in DKUM: | 27.02.2025 |
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| Views: | 151 |
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| Downloads: | 11 |
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| Metadata: |    |
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| Categories: | Misc.
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