materials Article Modeling and Composition Design of Low-Alloy Steel’s Mechanical Properties Based on Neural Networks and Genetic Algorithms Zhenlong Zhu 1,2,3, Yilong Liang 1,2,3,* and Jianghe Zou 1,2,3 1 College of Materials and Metallurgy, Guizhou University, Guiyang 550025, China;
[email protected] (Z.Z.);
[email protected] (J.Z.) 2 Guizhou Key Laboratory of Materials Strength and Structure, School of Mechanical Engineering, Guizhou University, Guiyang 550025, China 3 High Performance Metal Structure Material and Manufacture Technology National Local Joint Engineering Laboratory, School of Mechanical Engineering, Guizhou University, Guiyang 550025, China * Correspondence:
[email protected]; Tel.: +86-130-3782-6595 Received: 3 November 2020; Accepted: 23 November 2020; Published: 24 November 2020 Abstract: 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.