## 1 Introduction  
5 xxx series aluminum alloys--which contain magnesium (Mg) as the main alloying element--have good corrosion resistance, weldability, and strength to weight ratios [1, 2, 3], and they are also non-heat treatable. These advantages make such alloys premier materials for a wide range of applications, including vessel structures, ship construction, the automotive industry, the aircraft industry, the chemical industry, and food handling [2, 3, 4, 5]. As a result, 5 xxx aluminum alloys have attracted substantial research attention. For instance, the hot forming characteristics of the alloy have been examined in attempts to improve the production methods, reduce the cost, and improve the performance of aluminum products. To understand hot formability, it is essential to elucidate high-temperature deformation and flow stress behavior, and to derive the constitutive equation of the hot deformation. The constitutive equation explains the variation of stress as functions of various parameters, including strain rate, temperature, and strain. To date, many types of constitutive equations have been adopted to model the flow stress of the hot deformation of metallic alloys; the Arrhenius type constitutive equation based on the Zener-Hollomon theory has been the most widely used among them. For instance, [PERSON] et al. [6] studied the hot deformation of supersaturated Inconel 718 superalloy. In that study, the Inconel alloy was subjected to hot deformation tests at temperatures of 1100-1200 \({}^{\circ}\)C, and the flow stress was modeled using the Arrhenius type equation. [PERSON] et al. [7] examined Hastelloy C-276 alloy through hot tensile tests at temperatures ranging between 1223-1423 K and strain rates ranging between 0.01-10/s. In that study, the flow stress was modeled using the modified Zerilli-Armstrong model, the modifiedJohnson-Cook model, and the modified Arrhenius model. The research findings showed that the modified type of the Arrhenius model predicted the flow stress best. [PERSON] et al. [8] studied the high-temperature deformation of AISI 1035 steel. In that study, AISI 1035 steel was subjected to hot compression tests at temperatures ranging between 900-1100 \({}^{\circ}\)C and strain rates ranging between 0.2-20/s. They also accurately modeled the flow stress of AISI 1035 steel by adopting the Arrhenius constitutive equation. [PERSON] et al. [9] also used the Arrhenius equation to model the hot deformation flow stress of Nb-V-Ti micro-alloyed steel. In that study, hot compression tests were conducted on steel at temperatures ranging between 850-1100 \({}^{\circ}\)C and strains ranging between 1-100/s. The results showed that the Arrhenius type equation had a good level of performance. Traditionally, constitutive equations, including the Arrhenius equation, have been developed based on regression methods. However, regression-based constitutive equations often lack accuracy and efficiency in modeling complex flow stress behavior. Specifically, the relationships between flow stress and process parameters such as strain, strain rate, and temperature are highly nonlinear. Therefore, a new modeling method using the neural network approach has been adopted [10; 11; 12; 13; 14; 15]. For instance, [PERSON] et al. [10] studied the hot deformation of 7050 Aluminum Alloy. In that work, hot compression tests for 7050 aluminum alloy were conducted at strain rates ranging between 0.01-10/s and temperatures ranging between 573 K and 723 K. The flow stress of 7050 aluminum alloy during the hot deformation was modeled using the improved type of Arrhenius equation and the neural network model. The performance of the neural network model was found to be better than that of the Arrhenius equation. [PERSON] et al. [11] performed hot compression tests of Ti600 titanium alloy at temperatures ranging between 760-920 \({}^{\circ}\)C and strain rates ranging between 0.01-10/s. The flow stress of the Ti600 titanium alloy was modeled using the Arrhenius equation, multiple linear model, and neural network model.