###### 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. artificial neural networks; automatic explanation; hot extrusion; aluminum alloy; large language models; ChatGPT  
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consequently enhanced mechanical properties such as tensile strength and hardness, with a high predictive accuracy [9]. ANNs also predicted tool wear in aluminum matrix composites (AMC) by incorporating parameters such as vibration acceleration, cutting forces, and varying cutting speeds, thus contributing to improved machining processes and tool life optimization [10]. For friction stir welding (FSW) of aluminum joints, ANNs classified joint quality and regressed tensile load-bearing capacity, optimizing welding parameters with a high validation accuracy [11]. ANNs were able to predict the yield and ultimate tensile strength of metallic alloys, including aluminum, achieving greater than 95% confidence by leveraging chemical composition, tempers, and hardness data [12]. Similarly, ANNs optimized the chemical composition of new aluminum alloys, achieving a high prediction accuracy and efficiency in developing alloys with specific properties [13]. Additionally, ANNs predicted the flow behavior of AA5005 alloys at high temperatures and low strain rates more accurately than traditional methods; it was revealed that the BP-ANN model provides superior accuracy in predicting flow stress under diverse conditions [14]. The modified backpropagation ANN model, enhanced by a genetic algorithm [15], accurately predicted the flow behavior of 5754 aluminum alloy during hot compression tests; as such, it offers a method to optimize hot working parameters and improve materials. In the case of [16], deep learning techniques like convolutional neural networks (CNNs) replaced finite element analysis to predict residual stress distribution in aluminum alloy components. In the above research, ANNs were generally used to model the improved material characteristics of AA. So far, there has been little research on AA and other materials that, apart from the expert explanation given by the researchers, enables a more automated explanation for a better understanding of the physical phenomenon under consideration.