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. 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.
Highlights
Low-alloy steel is an important metal material in economic and defense contexts
The mechanical properties of alloy steels depend on the internal organization microstructure, and the internal organization depends on the influence of important factors, such as alloy elements and process parameters [1,2,3,4,5]
Since the mechanical properties of steel are usually determined by the internal organization, which depends on the chemical composition and process parameters, it is common to study the organizational properties or to change the processing conditions to determine their impact on the properties
Summary
Low-alloy steel is an important metal material in economic and defense contexts. The mechanical properties of alloy steels depend on the internal organization microstructure, and the internal organization depends on the influence of important factors, such as alloy elements and process parameters [1,2,3,4,5]. Materials 2020, 13, 5316 appropriate processing of actual mass production data, a composition design model with sufficient accuracy and reliability is constructed to achieve a predictable tensile strength, yield strength, and other performance indicators of steel products; the model should reasonably reveal the composition, process, and other parameters. The above modeling was done to obtain metallurgical trends that reflect the evolution of mechanical properties through designing physical experiments and transforming the trends into a mathematical model based on numerical simulation and other methods. These results can reflect the evolution of steel structures during the actual hot rolling process and have a certain reliability and universality. The above modeling is mainly for a single steel grade, and there are certain limitations in realizing a prediction of the structure and performance of multiple steel grades
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