Application of finite element method and artificial neural networks to predict the rolling force in hot rolling of Mg alloy plates

specific strength, high specific stiffness, and many other advantages (Mordike et al., 2001). With the increased consciousness of energy saving and environmental protection, Mg alloys are becoming widely recognized as the candidates to replace steel and aluminium alloys in many fields, such as in vehicles and the electronics industry. Mg alloys are thus becoming known as new ‘green’ engineering materials in the 21st century (Hosokawa et al., 2003). However, Mg and its alloys have very poor ductility at room temperature due to their hexagonal close-packed (HCP) crystal structure, and rolled Mg alloy sheets show a strong basal texture, therefore limiting their application (Thirumurugan et al., 2011). Much research has been done to study the deformation mechanism of Mg alloys, with the aim of improving the ductility and strength (Li et al., 2013; Choi et al., 2007; Choi et al., 2009). It has been shown that the microstructures, texture evolution, and mechanical properties of Mg alloys are highly reliant on their primary deformation process, such as differential-speed rolling (Lee et al., 2010), cross-rolling (Kang et al., 2008), accumulative roll-bonding (Saito et al., 1999), and asymmetrical rolling (Gao et al., 2002). The microstructures have been studied extensively and most of the technology still remains at the research stage. In order to expand the application of Mg alloy products, production of rolled Mg alloy sheets must be commercialized. The prediction model for rolling force is the crucial part of the computer-controlled system in the hot rolling of Mg alloys. Rolling force prediction methods include mathematical models, finite element models, intelligent methods (e.g. artificial neural networks, or ANNs), and a combination of ANNs and mathematical models. Using the mathematical model method to calculate rolling force is simple but has low precision. Using the finite element method (FEM) to simulate the rolling process decreases assumptions, but is timeconsuming and EMS (expanded memory system) memory-intensive (Sun et al., 2009). The intelligent method includes ANNs, genetic algorithms, and fish-net algorithms. Recently, a computational model combining a FEM with an ANN was used for various metallurgical problems (Esmailzadeh et al., 2012; Shabani et al., 2012) and prediction of mechanical properties (Shabani et al, 2011). ANN-FEM was used to predict residual stresses and the optimal cutting conditions during hard turning of bearing steel (Umbrello et al., 2007), springback prediction for incremental sheet forming (Han et al., 2013), and other aspects of metal forming processes (Kim et al., 2000). However, to the best of the authors’ knowledge, the computational method has rarely been used to Application of finite element method and artificial neural networks to predict the rolling force in hot rolling of Mg alloy plates