This study examines the high temperature deformation and constitutive modeling of flow stress for 310s stainless steel. To this end, hot tensile experiments were conducted under the temperatures of 700° and 800° at the strain rates of 0.0002/s, 0.002/s, and 0.02/s. Flow stress was modeled using the Arrhenius type constitutive equation and neural network approach. Specifically, Regime-Switched Neural Networks (RSNN), a set of neural networks with switching, has been newly proposed as a better predictive model for flow stress. The modeling performance of the RSNN model was evaluated through a comparison with traditional Arrhenius-type constitutive equations and the single neural network model. The results showed that the accuracy of the proposed RSNN was substantially higher than those of the existing Arrhenius-type equations, and the prediction performance was therefore significantly improved. In addition, the accuracy of the proposed RSNN was improved by approximately more than 24% in comparison with the existing global model—a single neural network—thus confirming the superiority of the proposed switching model.

中文翻译：

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机制切换神经网络：310s 不锈钢热变形过程中的流变应力建模策略


本研究研究了 310s 不锈钢的高温变形和流动应力本构模型。为此，在700°和800°温度下以0.0002/s、0.002/s和0.02/s的应变速率进行热拉伸实验。使用阿伦尼乌斯型本构方程和神经网络方法对流动应力进行建模。具体来说，新提出的机制切换神经网络（RSNN）是一组具有切换功能的神经网络，作为流动应力更好的预测模型。通过与传统的阿伦尼乌斯型本构方程和单一神经网络模型的比较来评估RSNN模型的建模性能。结果表明，所提出的 RSNN 的精度大大高于现有的阿伦尼乌斯型方程，预测性能因此得到显着提高。此外，与现有的全局模型（单个神经网络）相比，所提出的 RSNN 的准确性提高了约 24% 以上，从而证实了所提出的切换模型的优越性。