Abstract An artificial neural network (ANN) model was designed to correlate the complex relations among composition, temperature, and mechanical properties of 18Cr-12Ni-Mo austenitic stainless steels. The developed model was used to estimate the composition-property and temperature-property correlations with 97% and 91% accuracy, for train and unseen test datasets. The ANN predictions are more accurate with experimental results as compared with the calculated properties of the existing model. The effective response of the alloying elements on the mechanical properties at ambient as well as elevated temperatures was quantitatively estimated with the help of the index of relative importance (IRI). The calculated results of the ANN model beneficial for both researchers as well as designers to guide actual experiments. Hence, this proposed technique will be helpful in developing the components of austenitic stainless steel with desired properties.

中文翻译：

---

通过神经网络模拟奥氏体不锈钢的高温力学性能

摘要 设计人工神经网络(ANN) 模型来关联18Cr-12Ni-Mo 奥氏体不锈钢的成分、温度和机械性能之间的复杂关系。开发的模型用于估计成分-特性和温度-特性相关性，准确率分别为 97% 和 91%，用于训练和未见过的测试数据集。与现有模型的计算属性相比，人工神经网络预测的实验结果更准确。在相对重要性指数 (IRI) 的帮助下，合金元素对环境和高温下机械性能的有效响应进行了定量估计。ANN 模型的计算结果有利于研究人员和设计人员指导实际实验。因此，