SIMULATION ( IF 1.3 ) Pub Date : 2021-03-01 , DOI: 10.1177/0037549721995574 Yun Zhang 1 , Xiaojie Xu 1
Empirical equations, thermodynamics frameworks, and neural network modeling have been developed to predict steel martensite start temperature, , but they might not tend to generalize well when composition includes a wide range of alloying elements. In this study, we develop the Gaussian process regression (GPR) model to shed light on the relationship between alloying elements and temperature for steels. A total of 1119 steels with ranging from 153 K to 938 K are examined. The model has a high degree of accuracy and stability, contributing to fast low-cost temperature estimations.
中文翻译:
机器学习钢 温度
已经开发了经验方程,热力学框架和神经网络模型来预测钢的马氏体起始温度, ,但是当成分中包含多种合金元素时,它们可能无法很好地概括。在这项研究中,我们开发了高斯过程回归(GPR)模型,以阐明合金元素与合金之间的关系。钢的温度。共有1119种钢检查范围从153 K到938K。该模型具有较高的准确性和稳定性,有助于快速实现低成本 温度估算。