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.

Effect of Thermomechanical Treatment on Mechanical Behaviour and Microstructure of Low Alloy Steel

2012 ◽  
Vol 184-185 ◽  
pp. 838-849
Author(s):  
Mahmoud M. Tash
Keyword(s):  
Mechanical Properties ◽  
Heat Treatment ◽  
Impact Toughness ◽  
Alloy Steel ◽  
Mechanical Behaviour ◽  
Hot Forging ◽  
Weight Ratio ◽  
Ultimate Tensile Stress ◽  
Low Alloy Steel ◽  
Strength And Ductility

The present study was undertaken to investigate the effect of thermo-mechanical treatment (TMT) on the microstructure and mechanical behaviour of low alloy steel. Hot forging is carried out at 1200°C using mechanical press of 500 and 800 ton. The effect of hot forging reduction ratios (1.11 and 1.29) on the hardness and mechanical properties are studied. TMT samples are given different heat treatment i.e. annealing (A), normalizing (N), hardening (H), hardening and tempering (H/T) and their corresponding impact toughness are obtained. Selected heat treatment (normalizing and annealing) are given to tensile test samples and their corresponding strength and ductility are obtained. Ultimate tensile, 0.2% offset yield strength and percent elongation are measured. Hardness and impact toughness measurements were carried out for all alloy conditions. Hardness (HV), ultimate tensile stress (UTS-MPa) and 0.2% offset yield stress (MPa) increases with increasing reduction ratio. TMT leads to a sharp rise in alloy hardness and strength. Normalizing and annealing following TMT revealed a low hardness values compared to those observed in the TMT condition. Annealing reduces hardness and strength but increases ductility and impact toughness. This could be attributed to the recovery and coarsening effect. Pro-eutectoid ferrite phase are observed along the grain boundaries of low alloy steel in the TMT conditions regardless of the reduction ratios. Normalized samples show a refined pearlitic microstructure while coarse pearlite is observed in the annealed one. Good mechanical properties can be obtained by a combination of plastic deformation and thermal treatment. Heat treatment is one of the major factors used to enhance the mechanical properties of low alloy steel. An understanding of the combined effect of TMT and subsequent heat treatment on the structure and mechanical properties of low alloy steel would help in selecting conditions required to achieve the optimum mechanical properties and alloy high strength to weight ratio. This may be achieved by measuring hardness, impact toughness, strength and ductility resulting from different heat treatment following TMT.

Development of Line Pipes With Improved Behavior in Severe Sour Environment

2006 ◽  
Author(s):  
I. N. Veselov ◽  
I. Yu. Pyshmintsev ◽  
S. U. Zhukova ◽  
D. A. Pumpyanskyi ◽  
V. G. Antonov
Keyword(s):  
Mechanical Properties ◽  
Heat Treatment ◽  
Corrosion Resistance ◽  
Hydrogen Sulfide ◽  
Alloy Steel ◽  
Welded Joints ◽  
Low Carbon ◽  
Low Alloy Steel ◽  
Benchmark Tests ◽  
Treatment Conditions

A low-carbon low-alloy steel corresponding to Grade X42 according to API SPEC 5L and resistant to hydrogen sulfide environments has been developed. Heat treatment conditions have been optimized. The heat treatment recommended provides for the corrosion resistance and desired level of finished product mechanical properties. Welded joints of pipes made of the developed steel have successfully passed benchmark tests carried out at Astrakhan gas-condensate field.

Application of Artificial Neural Network Based on GA-BP Algorithm to Predicting Mechanical Properties of TC4

2010 ◽  
Vol 146-147 ◽  
pp. 1698-1701
Author(s):  
Zhe Zhe Hou ◽  
Yan Liang Du
Keyword(s):  
Neural Network ◽  
Mechanical Properties ◽  
Heat Treatment ◽  
Genetic Algorithm ◽  
Artificial Neural Network ◽  
Back Propagation ◽  
Optimization Methods ◽  
Calculation Results ◽  
Tc4 Alloy ◽  
Artificial Neural

On the basis of numerous experimental results the effect of heat treatment on mechanical properties of TC4 alloy is studied. A computer model expressing the relationships between heat treatment and mechanical properties has been established with a back propagation feed forword artificial neural network method. The optimization methods based on artificial neural network and the genetic algorithm, using binary system, optimize the weight and threshold by the genetic algorithm. The calculation results show that the model has good learning precision and generalization and it can be used for predicting the mechanical properties of TC4 alloy.

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