Mechanical properties of a low alloy steel in a molten nitrate salt environment

1985 ◽  
Vol 16 (6) ◽  
pp. 1031-1041
Author(s):  
S. H. Goods
Keyword(s):  
Mechanical Properties ◽  
Alloy Steel ◽  
Nitrate Salt ◽  
Low Alloy Steel

MICROSTRUCTURE AND MECHANICAL PROPERTIES OF 1500 MPa GRADE ULTRA--HIGH STRENGTH LOW ALLOY STEEL

2010 ◽  
Vol 46 (6) ◽  
pp. 687-694 ◽  
Author(s):  
Lijun WANG ◽  
Qingwu CAI ◽  
Wei YU ◽  
Huibin WU ◽  
Aidi LEI
Keyword(s):  
Mechanical Properties ◽  
Alloy Steel ◽  
High Strength ◽  
Low Alloy Steel ◽  
Microstructure And Mechanical Properties

Effect of controlled rolling on the mechanical properties of low-alloy steel H-beams

1986 ◽  
Vol 28 (4) ◽  
pp. 248-251
Author(s):  
V. M. Farber ◽  
V. N. Davydov ◽  
V. V. Machikhin ◽  
V. F. Evdokimov ◽  
E. G. Zudov
Keyword(s):  
Mechanical Properties ◽  
Alloy Steel ◽  
Controlled Rolling ◽  
Low Alloy Steel

Mechanical Properties of Layers Obtained by DC-Pulsed Plasma Nitriding on a Low-Alloy Steel (AISI 4140)

2009 ◽  
Vol 6 (S1) ◽  
pp. S314-S320 ◽  
Author(s):  
Luciano Dutrey ◽  
Evangelina De Las Heras ◽  
Hernán G. Svoboda ◽  
Pablo A. Corengia
Keyword(s):  
Mechanical Properties ◽  
Alloy Steel ◽  
Plasma Nitriding ◽  
Pulsed Plasma ◽  
Low Alloy Steel ◽  
Aisi 4140 ◽  
Steel Aisi

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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