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首页> 外文期刊>Journal of Materials Engineering and Performance >Identification of Multiple Parameters of the Bammann-Chiesa-Johnson Constitutive Model with Comprehensive Experiments for Pure Aluminum
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Identification of Multiple Parameters of the Bammann-Chiesa-Johnson Constitutive Model with Comprehensive Experiments for Pure Aluminum

机译：纯铝综合实验识别Bammann-Chiesa-Johnson本构模型的多个参数

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摘要

The flow stress is significantly affected in the precision machining process by the factors of strain path, strain rate history, temperature history and even microstructures. The physics-based Bammann-Chiesa-Johnson viscoplastic model is capable of capturing the flow stress evolution as well as the rate and temperature dependence. However, it is extremely challenging to identify all 18 constants in the model. In this paper, a novel method was proposed to identify the material constants by decoupling the parameters of internal state variables. A comprehensive experimental plan was conducted to obtain the true stress-strain curves of aluminum 1060 at elevated temperatures from 25 to 500 degrees C and strain rates from 10(-6) to 16400 s(-1) using creeping, quasi-static and SHPB methods. The particle swarm optimization algorithm was adapted to find the optimal solutions of the model parameters. The results showed that the prediction on the established model matched well with the experimental data. The quantitative error analysis confirmed the reliability of the method across a large range of strain rate and temperature variations.
机译：通过应变路径，应变率历史，温度历史甚至微观结构的因素，流量应力在精密加工过程中受到显着影响。基于物理的Bammann-Chiesa-Johnson粘胶塑料模型能够捕获流量应力进化以及速率和温度依赖性。但是，识别模型中的所有18个常量是非常具有挑战性的。在本文中，提出了一种新的方法来通过解耦内部状态变量的参数来识别材料常数。通过使用爬行，准静态和SHPB从25至500℃至16400秒（-1）的升高温度下，在升高的温度下获得综合实验计划，以获得从25至500℃至16400秒（-1）的升高温度。方法。粒子群优化算法适用于找到模型参数的最佳解决方案。结果表明，与实验数据相匹配的建立模型的预测。定量误差分析证实了跨越大范围的应变速率和温度变化的方法的可靠性。

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