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<p>Plasticity modeling at elevated temperatures is highly nonlinear and multivariate in nature; the accurate prediction of the strain hardening behavior is challenging. To address this issue, this study investigates the mechanical properties of AA6061-T6 round bars when exposed to high temperatures to assess their plastic behavior and thermal stability. Tensile tests over 25–250&#xa0;°C were performed at a fixed strain rate of 0.001/s to avoid confounding between temperature and strain rate; constitutive parameters were calibrated at this reference rate. The Johnson–Cook (JC), Zerilli-Armstrong (ZA), Khan-Huang-Liang (KHL), Lim-Huh (LH), and artificial neural network (ANN) models were utilized to model the true stress-plastic strain behavior at different temperatures. Finite element analyses were conducted to compute the reaction force using ABAQUS/Explicit VUMAT subroutines with all the analytical and ANN models. These models were evaluated for their accuracy in replicating experimental tensile behavior under high temperature. The analytical results showed that the ANN model has higher prediction accuracy, achieving a determination coefficient of 0.99998, whereas those of the JC, ZA, KHL, and LH models were found to be 0.9608, 0.7351, 0.9129, and 0.9614, respectively. The FEA results showed that the ANN model accurately illustrates the load capability, with the best agreement among the analytical models. The results highlight the robustness and predictive capability of the ANN model, making it a reliable tool for modeling intricate stress–strain relationships under high temperature.</p>

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Experimental characterization and numerical simulation of the thermoforming behavior of AA6061-T6 round bars at high temperatures

  • 期刊论文

  • 发表日期：

    2025年11月20日

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  • Thamer Sami Alhalaybeh,
  • Yanshan Lou

摘要

Plasticity modeling at elevated temperatures is highly nonlinear and multivariate in nature; the accurate prediction of the strain hardening behavior is challenging. To address this issue, this study investigates the mechanical properties of AA6061-T6 round bars when exposed to high temperatures to assess their plastic behavior and thermal stability. Tensile tests over 25–250 °C were performed at a fixed strain rate of 0.001/s to avoid confounding between temperature and strain rate; constitutive parameters were calibrated at this reference rate. The Johnson–Cook (JC), Zerilli-Armstrong (ZA), Khan-Huang-Liang (KHL), Lim-Huh (LH), and artificial neural network (ANN) models were utilized to model the true stress-plastic strain behavior at different temperatures. Finite element analyses were conducted to compute the reaction force using ABAQUS/Explicit VUMAT subroutines with all the analytical and ANN models. These models were evaluated for their accuracy in replicating experimental tensile behavior under high temperature. The analytical results showed that the ANN model has higher prediction accuracy, achieving a determination coefficient of 0.99998, whereas those of the JC, ZA, KHL, and LH models were found to be 0.9608, 0.7351, 0.9129, and 0.9614, respectively. The FEA results showed that the ANN model accurately illustrates the load capability, with the best agreement among the analytical models. The results highlight the robustness and predictive capability of the ANN model, making it a reliable tool for modeling intricate stress–strain relationships under high temperature.