The influence of different heat treatment cycles on the properties of the steels HARDOX® 500 and STRENX® 700

2020 ◽  
pp. 67-74
Author(s):  
Bruno Gallina ◽  
Luciano Volcanoglo Biehl ◽  
Jorge Luis Braz Medeiros ◽  
José de Souza
Keyword(s):  
Heat Treatment ◽  
Mechanical Strength ◽  
Alloy Steel ◽  
Vickers Microhardness ◽  
Rolling Process ◽  
Low Alloy Steel ◽  
High Mechanical Strength ◽  
Hot Rolling Process ◽  
Microstructural Morphology ◽  
Full Annealing

The HARDOX® 500 and STRENX® 700 steels are quenched materials from hot rolling process. The HARDOX® 500 is a low alloy steel with high values of microhardness and mechanical strength, the STRENX® 700 steel is a low alloy steel structural used in applications, where low density is associated with high mechanical strength. In this work, the two steels were submitted to different heat treatments: quenched and tempering, normalization and full annealing. The effects of these heat cycles were analyzed by optical microscopy and Vickers microhardness techniques. It was concluded that in the normalization treatment, the microhardness reduction of HARDOX® was more significant than that one of STRENX®. In the quenched and tempering process, both presented higher microhardness compared to the originally produced and characterized material with higher austenitic grain refining. The HARDOX® presented an effective increase of microhardness. In the heat treatment of full annealing, the HARDOX® 500 and the STRENX® 700 steels had similar microhardness and microstructural morphology values.

scholarly journals Investigation of the temperature distribution in seamless low-alloy steel pipes during the hot rolling process

2021 ◽  
Vol 2 ◽  
pp. 100038
Author(s):  
Raphael Langbauer ◽  
Georg Nunner ◽  
Thomas Zmek ◽  
Jürgen Klarner ◽  
René Prieler ◽  
...  
Keyword(s):  
Temperature Distribution ◽  
Alloy Steel ◽  
Hot Rolling ◽  
Rolling Process ◽  
Low Alloy Steel ◽  
Steel Pipes ◽  
Hot Rolling Process

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

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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