Zhu Zhenlong, Liang Yilong, Zou Jianghe
College of Materials and Metallurgy, Guizhou University, Guiyang 550025, China.
Guizhou Key Laboratory of Materials Strength and Structure, School of Mechanical Engineering, Guizhou University, Guiyang 550025, China.
Materials (Basel). 2020 Nov 24;13(23):5316. doi: 10.3390/ma13235316.
Accurately improving the mechanical properties of low-alloy steel by changing the alloying elements and heat treatment processes is of interest. There is a mutual relationship between the mechanical properties and process components, and the mechanism for this relationship is complicated. The forward selection-deep neural network and genetic algorithm (FS-DNN&GA) composition design model constructed in this paper is a combination of a neural network and genetic algorithm, where the model trained by the neural network is transferred to the genetic algorithm. The FS-DNN&GA model is trained with the American Society of Metals (ASM) Alloy Center Database to design the composition and heat treatment process of alloy steel. First, with the forward selection (FS) method, influencing factors-C, Si, Mn, Cr, quenching temperature, and tempering temperature-are screened and recombined to be the input of different mechanical performance prediction models. Second, the forward selection-deep neural network (FS-DNN) mechanical prediction model is constructed to analyze the FS-DNN model through experimental data to best predict the mechanical performance. Finally, the FS-DNN trained model is brought into the genetic algorithm to construct the FS-DNN&GA model, and the FS-DNN&GA model outputs the corresponding chemical composition and process when the mechanical performance increases or decreases. The experimental results show that the FS-DNN model has high accuracy in predicting the mechanical properties of 50 furnaces of low-alloy steel. The tensile strength mean absolute error (MAE) is 11.7 MPa, and the yield strength MAE is 13.46 MPa. According to the chemical composition and heat treatment process designed by the FS-DNN&GA model, five furnaces of Alloy1-Alloy5 low-alloy steel were smelted, and tensile tests were performed on these five low-alloy steels. The results show that the mechanical properties of the designed alloy steel are completely within the design range, providing useful guidance for the future development of new alloy steel.
通过改变合金元素和热处理工艺来精确改善低合金钢的力学性能备受关注。力学性能与工艺成分之间存在相互关系，且这种关系的机制较为复杂。本文构建的前向选择-深度神经网络与遗传算法（FS-DNN&GA）成分设计模型是神经网络与遗传算法的结合，其中由神经网络训练的模型被转移到遗传算法中。FS-DNN&GA模型利用美国金属学会（ASM）合金中心数据库进行训练，以设计合金钢的成分和热处理工艺。首先，采用前向选择（FS）方法，筛选并重新组合影响因素——碳（C）、硅（Si）、锰（Mn）、铬（Cr）、淬火温度和回火温度，作为不同力学性能预测模型的输入。其次，构建前向选择-深度神经网络（FS-DNN）力学预测模型，通过实验数据对FS-DNN模型进行分析，以最佳地预测力学性能。最后，将训练好的FS-DNN模型引入遗传算法中构建FS-DNN&GA模型，当力学性能增加或降低时，FS-DNN&GA模型输出相应的化学成分和工艺。实验结果表明，FS-DNN模型在预测50炉低合金钢的力学性能方面具有较高的准确性。抗拉强度平均绝对误差（MAE）为11.7MPa，屈服强度MAE为13.46MPa。根据FS-DNN&GA模型设计的化学成分和热处理工艺，冶炼了5炉Alloy1-Alloy5低合金钢，并对这5种低合金钢进行了拉伸试验。结果表明，设计的合金钢力学性能完全在设计范围内，为新型合金钢的未来发展提供了有益的指导。
