A high-strength, high-temperature-resistant rafm steel and its design method based on machine learning

A machine learning and design method technology, applied in the field of steel materials, can solve complex and time-consuming problems, and achieve good tensile properties and strong generalization capabilities

Active Publication Date: 2022-04-08
HEFEI INSTITUTES OF PHYSICAL SCIENCE - CHINESE ACAD OF SCI
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  • Abstract
  • Description
  • Claims
  • Application Information

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Problems solved by technology

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  • A high-strength, high-temperature-resistant rafm steel and its design method based on machine learning
  • A high-strength, high-temperature-resistant rafm steel and its design method based on machine learning
  • A high-strength, high-temperature-resistant rafm steel and its design method based on machine learning

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Embodiment 1

[0053] A high-strength, toughness and high-temperature-resistant RAFM steel in this embodiment is designed by using the method of the present invention, and the smelting and subsequent heat treatment are carried out in strict accordance with the composition and heat treatment parameters designed in the present invention.

[0054] The RAFM steel includes the following chemical components in mass percent: 0.12%C, 10.5%Cr, 1.6%W, 0.52%Si, 0.6%Mn, 0.27%V, 0.26%Ta, 0.001%Zr, 0.01%Y, 0.002% %Ti, 0.04% N, and the rest are Fe elements and other unavoidable impurity elements. The heat treatment parameters of the RAFM steel are: normalizing temperature (NT) 1050°C, normalizing time (Nt) 20min, tempering temperature (TT) 600°C, tempering time (Tt) 90min.

Embodiment 2

[0056] A high-strength, toughness and high-temperature-resistant RAFM steel in this embodiment is designed by using the method of the present invention, and the smelting and subsequent heat treatment are carried out in strict accordance with the composition and heat treatment parameters designed in the present invention.

[0057] The RAFM steel includes the following chemical components in mass percent: 0.16%C, 9.5%Cr, 1.8%W, 0.48%Si, 0.7%Mn, 0.23%V, 0.10%Ta, 0.005%Zr, 0.05%Y, 0.012% %Ti, 0.02% N, the rest is Fe and other unavoidable impurity elements. The heat treatment parameters of the RAFM steel are: normalizing temperature (NT) 1010°C, normalizing time (Nt) 55min, tempering temperature (TT) 720°C, tempering time (Tt) 50min.

Embodiment 3

[0059] A high-strength, toughness and high-temperature-resistant RAFM steel in this embodiment is designed by using the method of the present invention, and the smelting and subsequent heat treatment are carried out in strict accordance with the composition and heat treatment parameters designed in the present invention.

[0060] The RAFM steel includes the following chemical components in mass percent: 0.13%C, 9.5%Cr, 1.7%W, 0.49%Si, 0.67%Mn, 0.25%V, 0.20%Ta, 0.002%Zr, 0.03%Y, 0.005% %Ti, 0.021% N, and the rest are Fe and other unavoidable impurity elements. The heat treatment parameters of the RAFM steel are: normalizing temperature (NT) 1010°C, normalizing time (Nt) 36min, tempering temperature (TT) 660°C, tempering time (Tt) 76min.

[0061] In the above-mentioned Examples 1-3, other unavoidable impurity elements are Al, Ni, Mo, Nb, Cu, B, S, P, O, and the proportion of each impurity element is: Al: 0-0.010%, Ni: 0~0.005%, Mo: 0~0.005%, Nb: 0~0.010%, Cu: 0~0.010%, B: 0~0.0...

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Abstract

The invention provides a high-strength, toughness and high-temperature resistant RAFM steel, the chemical composition of which is: C: 0.12-0.16%, Cr: 9.5-10.5%, W: 1.6-1.8%, Si: 0.48-0.52%, Mn: 0.6- 0.7%, V: 0.23~0.27%, Ta: 0.10~0.26%, Zr: 0.001~0.005%, etc.; heat treatment parameters are: normalizing temperature 1010~1050℃, normalizing time 20~55min, tempering temperature 600~720 ℃, tempering time 50 ~ 90min. The present invention also provides a machine learning-based design method for the above-mentioned steel. The RAFM steel of the present invention has a higher service temperature upper limit, and also has better tensile properties at high temperatures; at the same time, the machine learning method constructed by the present invention has strong generalization ability, and can quickly and efficiently design reasonable composition and heat treatment parameter.

Description

technical field [0001] The invention relates to the technical field of iron and steel materials, in particular to a high-strength, high-temperature-resistant RAFM steel and a design method based on machine learning. Background technique [0002] Low-activation ferritic / martensitic (RAFM) steels developed from conventional 9Cr-1Mo steels are considered to be very promising structures for fusion reactors due to their good thermophysical, thermomechanical, and radiation resistance properties Material. As a metallic structural material, tensile properties are the most important fundamental factor in the design and application evaluation of RAFM steel. In fusion reactors, the improvement of high-temperature mechanical properties of structural materials is conducive to improving the utilization efficiency of nuclear energy. At present, the upper limit of the service temperature of RAFM steel is 550°C. In order to optimize the high temperature tensile properties of RAFM steels, ...

Claims

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Application Information

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Patent Type & Authority Patents(China)
IPC IPC(8): C22C38/02C22C38/04C22C38/22C22C38/24C22C38/26C22C38/28C21D1/18C21D1/28C21D6/00C21D11/00G16C60/00G16C20/70G16C20/50G16C20/30G06N20/20G06N20/10G06N5/00G06N3/08
CPCC22C38/22C22C38/02C22C38/04C22C38/24C22C38/26C22C38/28C22C38/005C22C38/001C21D1/28C21D1/18C21D11/00C21D6/002C21D6/005C21D6/008G16C60/00G16C20/70G16C20/50G16C20/30G06N20/20G06N20/10G06N3/08G06N5/01
Inventor 郑明杰李孝晨丁文艺
Owner HEFEI INSTITUTES OF PHYSICAL SCIENCE - CHINESE ACAD OF SCI
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