New Approach for Automated Explanation of Material Phenomena (AA6082) Using Artificial Neural Networks and ChatGPT
Abstract
:1. Introduction
2. Materials and Methods
2.1. Description of AA6082
2.2. CAE Artificial Neural Network
- CAE ANN predicts unknown (k-th) output parameter as a mean value by using the general formula [5]:
- The local standard error (also, standard deviation) of the prediction is calculated by using the following formula [35]:
- An estimation of the reliability of the predicted mean value based on data density is calculated by using the following formula [5]:
2.3. General Framework for the Automatic Explanation of the Obtained Results by the (CAE) ANN
3. Results
3.1. Rules for Explaining the Results
3.2. Illustration of Explanations for Various Instances of Graphical Displays of Results for Hot Extrusion of AA6082
- Elongation: The predicted elongation at this point is approximately 11.95. This is the expected outcome of the elongation test given these specific conditions of casting speed and Si content.
- Standard Deviation: The standard deviation associated with this prediction is not directly quoted, but it can be inferred from the provided text files that it would likely be a value close to the nearby standard deviation values. A lower standard deviation would indicate that the predicted elongation value of 11.95 is more reliable.
- Data Density: The data density at this point is 0.80 This value indicates the number of data points or the amount of information from the experiments or simulations that contributed to the prediction at this specific point. A higher data density usually means that the prediction is based on more information and could be considered more reliable.
- Increasing Casting Speed (Si constant at 0.9 wt.%): At a constant silicon content of 0.9 wt.%, as the casting speed increases from 7.4 mm/s to the right, the solid lines representing elongation curve upwards. This means that elongation increases with casting speed in that range. Therefore, if you increase the casting speed while maintaining the Si content at 0.9 wt.%, the prediction for elongation will be higher.
- Increasing Si Content (Casting Speed constant at 7.4 mm/s): When the casting speed is held constant at 7.4 mm/s, and we look at increasing Si content vertically on the graph, the solid isolines for elongation dip downward as we move up, which indicates that elongation decreases as the Si content increases at this casting speed.
- High yield strength (higher values on the graph),
- Lower standard deviation (indicating consistency in the data),
- Acceptable levels of elongation (based on the previous elongation graph), and
- High data density (darker areas on the graphs).
- 1.
- Sensitivity and Predictability: With the additional fixed parameters (ram speed and Mn content), the sensitivity and predictability of yield strength to changes in Si con-tent and casting speed may differ. The shape and spacing of the isolines in the new graphs suggest that the relationships between these variables and yield strength have changed.
- 2.
- Effect of Fixed Parameters: By fixing ram speed and Mn content, we are looking at a more specific scenario in the casting process. These fixed conditions seem to alter the response of the material’s yield strength to changes in Si content and casting speed, as compared to when these parameters were not fixed.
- 3.
- Data Density and Reliability: Assuming data density is represented by the shading in both sets of graphs, it appears that predictions are most reliable in the regions with darker shading. It’s important to consider this when evaluating the yield strength at any given point on the graph.
4. Discussion
- A tiny fraction of knowledge from the existing global knowledge repository (for example, comments on likely microstructural level causes for predicted results) has already been included in the explanation, as ChatGPT has information from many publicly available scientific and other sources. The authors argue that, in order to provide a more objective interpretation of the (CAE) ANN predictions, a language that is otherwise “less beautiful” and stricter, specialized interpretation software with fewer linguistic masks will need to be developed.
- The incorporation of all available knowledge is intended, with deliberate gathering, verification, and then integration of specific knowledge from the field under consideration. Such an example would be, e.g., supplementing the automatic explanation with complex chemical reactions and/or mechanical microstructural phenomena as seen through the eyes of a human expert with many years of experience and knowledge, as explicitly stated in Section 2.1 when explaining the effect of important phenomena during hot extrusion of AA6082.
- An explanation tailored to various needs and knowledge levels should be possible with the development of an application, or specific parts of an application, in connection with ChatGPT (or other large language models). These applications will likely be closed and intended for a specific public (engineers, researchers, students) at different quality levels. Because the explanation’s language will be more objective, there will be a far lower possibility of automatic (machine) explanations being misunderstood. Additionally, if needed, this will guarantee the confidentiality of certain knowledge, which is a business secret.
5. Conclusions
- A general concept is proposed to explain the observed phenomena in metallic materials. The proposed framework aims to mimic the traditional scientific approach, which is based on mathematical representations of the considered physical phenomena.
- The proposed framework is applied to the example of hot extrusion of AA6082, where CAE is used as an artificial neural network to model the phenomenon and predict its key parameters.
- As a byproduct of the CAE ANN intermediate computation, values are often provided in conjunction with the CAE ANN predicted outcomes. Small values are typically used to define the limits of the CAE models.
- ChatGPT, as one of the publicly accessible large language model tools, is used to explain/interpret the CAE ANN predicted results according to the proposed framework.
- The obtained results are discussed, and some recommendations for improving the proposed framework are made.
- The basic idea is to enable researchers and practicing engineers to better understand the physical phenomena under consideration and the results provided by empirical models made with ANNs.
Supplementary Materials
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Acknowledgments
Conflicts of Interest
References
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Goričan, T.; Terčelj, M.; Peruš, I. New Approach for Automated Explanation of Material Phenomena (AA6082) Using Artificial Neural Networks and ChatGPT. Appl. Sci. 2024, 14, 7015. https://doi.org/10.3390/app14167015
Goričan T, Terčelj M, Peruš I. New Approach for Automated Explanation of Material Phenomena (AA6082) Using Artificial Neural Networks and ChatGPT. Applied Sciences. 2024; 14(16):7015. https://doi.org/10.3390/app14167015
Chicago/Turabian StyleGoričan, Tomaž, Milan Terčelj, and Iztok Peruš. 2024. "New Approach for Automated Explanation of Material Phenomena (AA6082) Using Artificial Neural Networks and ChatGPT" Applied Sciences 14, no. 16: 7015. https://doi.org/10.3390/app14167015
APA StyleGoričan, T., Terčelj, M., & Peruš, I. (2024). New Approach for Automated Explanation of Material Phenomena (AA6082) Using Artificial Neural Networks and ChatGPT. Applied Sciences, 14(16), 7015. https://doi.org/10.3390/app14167015