Effect of heat treatment on radiation embrittlement of low-alloy steel

1977 ◽  
Vol 19 (1) ◽  
pp. 38-41
Author(s):  
I. V. Gorynin ◽  
A. M. Morozov ◽  
V. A. Nikolaev
Keyword(s):  
Heat Treatment ◽  
Alloy Steel ◽  
Radiation Embrittlement ◽  
Low Alloy Steel

scholarly journals Normalizing heat treatment effect on low alloy steel weld metals

2004 ◽  
Vol 26 (1) ◽  
pp. 62-66 ◽  
Author(s):  
V. B. da Trindade Filho ◽  
A. S. Guimarães ◽  
J. da C. Payão Filho ◽  
R. P. da R. Paranhos
Keyword(s):  
Heat Treatment ◽  
Alloy Steel ◽  
Treatment Effect ◽  
Steel Weld ◽  
Low Alloy Steel ◽  
Weld Metals

Composition effects in the radiation embrittlement of low-alloy steel

Atomic Energy ◽  
1998 ◽  
Vol 84 (4) ◽  
pp. 304-307 ◽  
Author(s):  
A. M. Kryukov ◽  
Yu. A. Nikolaev ◽  
A. V. Nikolaeva
Keyword(s):  
Alloy Steel ◽  
Radiation Embrittlement ◽  
Low Alloy Steel ◽  
Composition Effects

scholarly journals Modeling and Composition Design of Low-Alloy Steel’s Mechanical Properties Based on Neural Networks and Genetic Algorithms

Materials ◽  
2020 ◽  
Vol 13 (23) ◽  
pp. 5316
Author(s):  
Zhenlong Zhu ◽  
Yilong Liang ◽  
Jianghe Zou
Keyword(s):  
Neural Network ◽  
Mechanical Properties ◽  
Heat Treatment ◽  
Genetic Algorithm ◽  
Alloy Steel ◽  
Deep Neural Network ◽  
Treatment Process ◽  
Mechanical Performance ◽  
Low Alloy Steel ◽  
Forward Selection

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.

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