## 1 Introduction
[46], strain has been mostly uniformly distributed along route BC following the third pass through the cross-sectional area, whereas strain is mainly localized along route A in the upper left location and, in the case of route C, at the corners. It is common knowledge that ECAP makes Mg alloys longer-lasting and stronger by improving their texture and refining their grains. In addition, it was found that the regular shift in orientation and shear surface throughout the overall procedure was caused by the various adopted ECAP routes with several passes, resulting in the greatest impact on refining grains [47]. Non-basal slip methods stimulation and the recently created texture were also observed to decrease the compressive behavior of the ECAP pure Mg as part of its mechanical attributes, obtained from extrusion without heating when \(\phi\) was set at 90\({}^{\circ}\) utilizing route BC for two passes. Refining grains, meanwhile, led to an improvement in mechanical behavior following 4P [48]. In another instance, the ECAP method was used with the AA2014 aluminum alloy by [PERSON] et al. [49]. It was shown in their work that the effective strain was equalized across all planes during processing by route Bc, and the mechanical behavior showed drastic enhancement as opposed to routes A, C, and Ba. The influence of \(\phi\) on the strain uniformity, development of microstructure, texture crystallography, and mechanical behavior of pure Mg was investigated by [PERSON] et al. [50]. The test took place with \(\phi\) = 90\({}^{\circ}\) and 120\({}^{\circ}\), and the specimens went via 4P of the Bc route. The analysis of microstructure and mechanical behavior measurements also indicated that the \(\phi\) = 90\({}^{\circ}\) specimen had a finer grain and greater ultimate strength than the \(\phi\) = 120\({}^{\circ}\) specimen. In order to discover and create novel materials with exceptional features, researchers have placed a lot of time and effort into developing various techniques for designing materials, such as experimental design procedures, approaches to optimizing parameters, and tangible physical modeling [51; 52; 53]. In order to discover the right connections across intricate features of materials and conceptual designs, conventional methods often necessitate a thorough, tangible, physical examination. There are a variety of laws of physics and chemistry that govern material features that must be explored in this context. Machine learning (ML) models are another option; these models just need a dataset to capture the understanding of the links among material attributes and design parameters, and they have lately shown great success in the field of material design. During the past few years, there has been a lot of matter about using ML in a wide variety of commercial settings, thanks in large part to the exponential growth in computational power, the accessibility of large databases, and the ease with which complex systems can be placed into practice. As a result, ML approaches are increasingly placed into practice to deal with large databases featuring rapid input flows and to carry out a variety of difficult tasks, including regression, categorization, data grouping, and dimensionality reduction. ML has advanced to the point that it can now handle intricate systems and expose complicated material systems, even with frequent transitions, by mining data for insights and making predictions about how the system will operate. Feeding fresh datasets to trained and well-tested ML models allows them to operate as a data analysis tool to independently extract outputs [54; 55]. Data on ECAP's effectiveness in terms of numbers is scarce in the literature. Because of its weak ability to deform, there are not many experiments that have looked at the way the ECAP method affects pure Mg; instead, researchers have mainly concentrated on Mg alloys with a \(\phi\) of less than 90\({}^{\circ}\).