## 4 Discussion
With these graphs, we can deduce that, at a Mn content of 0.48 wt.% and a ram speed of 15 mm/s, while keeping the yield strength constant at 322 MPa, the predicted silicon content is approximately 0.885 wt.%, with a standard deviation of about 0.081 wt.%. The prediction's precision is reasonably good, as indicated by the relatively low standard deviation. In summary, the proposed general framework enables the use of the (CAE) ANN to explain the predicted results in addition to their graphical presentation. It can be concluded that it represents a substantial advancement and simplification based on the prior studies [5; 6; 20; 31; 38], where the explanations/conclusions were typically produced based on an expert assessment of the obtained results. Compared to existing XAI approaches [41; 42], the suggested framework attempts to imitate the standard scientific methodology, which is based on mathematical representations of the considered physical phenomena with the lowest number of parameters and their derivatives. Furthermore, using the predicted values, we attempt to analyze the reliability and accuracy of the results, which are related to well-known statistical metrics. Thus, in place of the standard deterministic description, we are passing to the sphere of the probabilistic description of the phenomenon, which provides a more appropriate description and handling of phenomena in physical reality. Note that ANNs are used to describe physical phenomena using databases that contain both aleatory and epistemic uncertainties. These should not be neglected and must be addressed appropriately. The article in general does not deal with the common influence of casting speed and other parameters on mechanical properties. AA6082 was selected as an example for the presentation of the relevance of our proposed general framework for automated explanation of the observed phenomena. Authors are very aware that casting speed has a relevant influence on cooling conditions of melted alloy and consequently also so on characteristics of precipitated phases as well as on obtained mechanical properties. Thus, revealing the common influence of casting speed in relation to chemical composition and/or other process parameters on mechanical properties is very important since this is related to the characteristics of precipitated phases. Mechanical properties can be improved by finding the global optimum regarding different parameters, i.e., optimal value for each influential parameter for simultaneously achieving the highest values for yield strength and elongation. This also depends on the collected data database which increases constantly. Thus, the process of optimization is long-term and iterative. And this is where the proposed framework can help: a researcher or process engineer can try to understand the various relationships between the parameters when studying the phenomenon and, taking into account all the boundary conditions (technological, financial,...), and then optimize the production for, in the case of AA6082, the optimal yield in terms of the best mechanical properties. The presented examples of explanations show that it is possible to speed up the interpretation of the obtained results. In the standard procedure (i.e., [5]), such an analysis required a great deal of laborious work, including reading values, carefully observing the graphs, and recording the discovered laws and/or rules. The proposed framework and its application in various forms (different applications of _large language models_ and/or other applications of XAI and/or ANNs for the interpretation of results in specific areas) can make it easier for engineers to learn, comprehend, and ultimately optimize processes, some of which they encounter daily in their work, by simplifying the process of understanding and writing reports. The application of the proposed framework can make several operations much easier for researchers and professional engineers, towards whom the application is primarily aimed.