Constitutive models are considered a key prerequisite to investigate the thermal deformation behavior of materials. Accurate identification of their parameters is crucial for enhancing the predictive precision of the models. A novel optimization method for accurate inverse identification of parameters using the genetic algorithm (GA) in the Modified Zerilli-Armstrong (MZA) model was proposed in this work. Under conditions of temperatures ranging from 1223 K to 1473 K at intervals of 50 K and strain rates of 0.01, 0.1, 1, and 5 s(-1), uniaxial isothermal hot compression tests were performed on 2Cr13 martensitic stainless steel (MSS) employing a Gleeble-1500D thermal simulation test machine. Based on the experimental data, the conventional linear regression method was utilized to solve the MZA model of 2Cr13 MSS. Initial values for the material constants I-1, S-1, C-5, and C-6 to be optimized were assigned drawing from the calculated results. The GA-based iteratively optimized MZA (G-ZA) model was established by minimizing the mean squared error as an objective function between the experimental and predicted flow stress values. Compared to the MZA model, a significant enhancement in predictive performance was achieved with the G-ZA model, while good generalization was also demonstrated. Both models were successfully integrated into the Forge (R) finite element analysis software through the secondary development of user subroutines. Numerical simulation results indicated that the G-ZA model demonstrated a better agreement with the experimental data in predicting the load-displacement curve. This validated that a more accurate constitutive model for the hot deformation of 2Cr13 MSS can be effectively constructed using the GA-based parameter inverse identification strategy.

Ti6AL4V alloy is widely used in the biomedical and energy vehicle industries, among others. Ti6Al4V alloy cannot be fabricated at ambient temperatures; hence, it requires hot forming. However, this method is susceptible to crack defects. The crack defect problem of Ti6AL4V alloy in the hot-forming process cannot be ignored, so we must develop a precise hot-forming damage prediction model. In this study, three high-temperature damage models of Ti6Al4V alloy were developed, considering the temperature and strain rate. These models were derived from the normalized Cockcroft and Latham (NCL), Oyane, and Rice and Tracey (RT) damage models. The damage parameters of the models were identified using a genetic algorithm combined with finite element simulation. The force accumulation error of the Ti6AL4V alloy specimen, which was obtained from a simulated thermal tensile test and an actual test, was used as an optimization target function. Then, the damage parameters were optimized using the genetic algorithm until the target function reached the minimum value. Finally, the optimal damage model parameter was obtained. Through program development, the three high-temperature damage models established in this paper were embedded into Forge® NxT 2.1 finite element software. The simulated thermal tensile test of Ti6AL4V alloy was performed at a temperature of 800–1000 °C and a strain rate of 0.01–5 s−1. The simulated and actual fracture displacements of the tensile specimens were compared. The correlation coefficients (R) were calculated, which were 0.997, 0.951, and 0.912. Of the high-temperature damage models, the normalized Cockcroft and Latham high-temperature damage model had higher accuracy in predicting crack defects of Ti6Al4V alloy during the hot-forming process. Finally, a fracture strain graph and a high-temperature damage graph of Ti6Al4V alloy were constructed. The Ti6Al4V alloy damage evolution and thermal formability were analyzed in relation to the temperature and strain rate.

为了研究X12 合金钢的高温拉伸行为,在Gleeble-1500D热/力模拟试验机上进行了温度为900～1200℃、应变速率为0.01～5 s-1 的等温拉伸试验,分析了变形参数对该材料高温拉伸行为的影响规律.为了精确确定材料本构模型参数,提出了一种基于多岛遗传算法的反求优化方法.基于拉伸试验数据,采用提出的本构模型参数反求优化方法建立了X12 合金钢Voce本构模型.结果表明,X12合金钢的高温拉伸行为呈现出典型的加工硬化和动态回复特性,其流动应力受到温度和应变速率的显著影响.模型预测值与试验值之间的相关系数、均方根误差以及相对误差分布的期望值和标准偏差分别为 0.9933、6.36 MPa、0.3057和6.2998,说明采用反求优化方法得到的X12合金钢Voce本构模型能够准确地预测该材料的高温变形行为.

A precise constitutive model is the foundation and key to finite element simulation in material volume forming and the optimization of the hot working process. Hence, to build a precise constitutive model, a method based on a genetic algorithm (GA) for the inverse optimization identification of parameters is presented in this paper. The idea of this method is to continuously adjust the model parameters through GA until the objective function reaches the minimum value. In this study, hot compression experiments were performed on the Gleeble-1500D thermal simulator at temperatures ranging from 800 °C to 1000 °C and strain rates of 0.01 s−1 to 1 s−1. The Arrhenius-type (A-T) model considering strain compensation and the Johnson–Cook (JC) model considering the coupling effects of strain, temperature and strain rate were constructed, respectively, by using the regression method and the parameter inverse optimization identification method. For the purposes of comparing and verifying the reliability of the predictions of the two established constitutive models, the correlation coefficient (R), average absolute relative error (AARE), and relative error (RE) were adopted. The results show that both the optimized A-T model and the optimized JC model have high prediction accuracy. Compared to the optimized JC model, the optimized A-T model demonstrated a higher correlation coefficient, by 0.003, and a lower average absolute relative error, by 1.43%. Furthermore, the relative error distribution of the optimized A-T model was found to be more concentrated than that of the optimized JC model. These results suggest that the A-T model is more appropriate than the JC model for characterizing the high-temperature deformation behavior of Ti6Al4V alloy.