The popular constitutive models used in the field of hot forming of magnesium alloys can be divided into phenomenological models, machine learning models, and internal state variables (ISV) models based on physical mechanisms. Currently, there is a lack of comparison and evaluation regarding the suitability of different types of models. In this study, Mg-Gd-Y-Zr alloy is taken as the research object. The hot deformation behavior of the alloy was studied systematically. Subsequently, Arrhenius model with strain compensation, artificial neural network (ANN) model, and ISV model involving dynamic recrystallization (DRX), dislocation density and grain size evolution were established. ANN model demonstrates a higher level of accuracy in fitting the original stress-strain curves compared to both ISV model and modified Arrhenius model, but ANN model is not suitable for predicting the experimental results outside of the initial database. ISV model considers the impact of microstructure evolution history on stress, making it highly effective in reflecting the mechanical responses under complex loading condition. The established ISV model is embedded in the ABAQUS software, which shows good ability in calculating the mechanical response, dimension, and microstructure evolution information of the component during hot forming.

中文翻译：

---

预测 Mg-Gd-Y-Zr 合金热变形行为的不同本构模型的比较与评价


镁合金热成型领域常用的本构模型可分为现象学模型、机器学习模型和基于物理机制的内部状态变量 （ISV） 模型。目前，缺乏关于不同类型模型的适用性的比较和评估。本研究以 Mg-Gd-Y-Zr 合金为研究对象。系统研究了合金的热变形行为。随后，建立了具有应变补偿的 Arrhenius 模型、人工神经网络 （ANN） 模型和涉及动态再结晶 （DRX） 、位错密度和晶粒尺寸演变的 ISV 模型。与 ISV 模型和改进的 Arrhenius 模型相比，ANN 模型在拟合原始应力-应变曲线方面表现出更高的准确性，但 ANN 模型不适合在初始数据库之外预测实验结果。ISV 模型考虑了微观结构演变历史对应力的影响，使其在反映复杂载荷条件下的机械响应方面非常有效。建立的 ISV 模型嵌入到 ABAQUS 软件中，在热成型过程中计算组件的机械响应、尺寸和微观结构演变信息方面表现出良好的能力。