机器学习辅助<strong>2000 MPa</strong>级弹簧钢成分和热处理工艺开发

doi:10.11900/0412.1961.2022.00047

金属学报

2023, Vol. 59

Issue (11): 1499-1512 DOI: 10.11900/0412.1961.2022.00047

本期目录 | 过刊浏览

机器学习辅助2000 MPa级弹簧钢成分和热处理工艺开发

杨累¹^,², 赵帆¹^,²^,³(

), 姜磊¹^,², 谢建新¹^,²^,⁴

^1.北京科技大学新材料技术研究院现代交通金属材料与加工技术北京实验室北京 100083
^2.北京科技大学新材料技术研究院材料先进制备技术教育部重点实验室北京 100083
^3.东北轻合金有限责任公司哈尔滨 150060
^4.北京科技大学新材料技术研究院北京材料基因工程高精尖创新中心北京 100083

Development of Composition and Heat Treatment Process of 2000 MPa Grade Spring Steels Assisted by Machine Learning

YANG Lei¹^,², ZHAO Fan¹^,²^,³(

), JIANG Lei¹^,², XIE Jianxin¹^,²^,⁴

^1.Beijing Laboratory of Metallic Materials and Processing for Modern Transportation, Institute for Advanced Materials and Technology, University of Science and Technology Beijing, Beijing 100083, China
^2.Key Laboratory for Advanced Materials Processing (MOE), Institute for Advanced Materials and Technology, University of Science and Technology Beijing, Beijing 100083, China
^3.Northeast Light Alloy Co., Ltd., Harbin 150060, China
^4.Beijing Advanced Innovation Center for Materials Genome Engineering, Institute for Advanced Materials and Technology, University of Science and Technology Beijing, Beijing 100083, China

引用本文:

杨累, 赵帆, 姜磊, 谢建新. 机器学习辅助2000 MPa级弹簧钢成分和热处理工艺开发[J]. 金属学报, 2023, 59(11): 1499-1512.
Lei YANG, Fan ZHAO, Lei JIANG, Jianxin XIE. Development of Composition and Heat Treatment Process of 2000 MPa Grade Spring Steels Assisted by Machine Learning[J]. Acta Metall Sin, 2023, 59(11): 1499-1512.

全文: PDF(5993 KB) HTML

摘要:

通过收集弹簧钢及其他典型淬火+回火型钢铁材料的文献数据，采用面向性能的机器学习设计系统(MLDS)结合实验优化，实现了具备超高强度和良好塑性的新型弹簧钢化学成分及热处理工艺参数的快速设计。所开发的2种新型弹簧钢的抗拉强度分别为2183.5和2193.0 MPa、屈服强度分别为1923.0和2024.5 MPa、断后伸长率分别为10.5%和9.7%、断面收缩率分别为42.4%和41.5%。新型弹簧钢的强化方式以晶界强化和位错强化为主，细小的晶粒尺寸和适量的奥氏体使得弹簧钢在具备超高强度的同时保持良好的塑性。与现有同等强度级别的超高强度钢相比，新型弹簧钢具有显著的成本优势和工艺优势。

关键词 ：弹簧钢, 超高强, 热处理, 机器学习

Abstract：

The rapid development of rail transit has led to the proposition of higher requirements for the mechanical properties of springs and spring steels. Thus, bogies have been identified as the key components for trains to achieve high speed since they are connected with train bodies and wheel sets through springs. Alternatively, since the properties of spring steel materials have an important effect on the safety and comfort of high-speed trains, the development of spring steels with ultra-high strength and good plasticity has attracted the attention of researchers and industrial circles. However, simultaneously improving strength and plasticity has remained an important challenge for the research and development of high-end steels. Notwithstanding, machine learning has recently made substantial progress in designing and predicting various materials, and is expected to become a powerful tool for clarifying the relationship between the composition, process, and properties of complex alloys like steels. Based on the above background, this study reports the realization of rapid chemical composition and heat treatment process-design parameters for new spring steels, using a performance-oriented machine learning design system with high strength and good plasticity (tensile strength (2050 ± 50) MPa, elongation 10.5% ± 1.5%) after collecting literature data on spring steels and other typical quenched + tempered steels. Experimental studies were also carried out to obtain a further optimized heat treatment process (heating at 950^oC for 30 min and oil quenching + tempering at 380^oC for 90 min and water cooling). Investigations revealed that the tensile strengths of the two new spring steel materials developed were 2183.5 and 2193.0 MPa, their yield strengths were 1923.0 and 2024.5 MPa, their elongations after fracture were 10.5% and 9.7%, and the area reductions were 42.4% and 41.5%, respectively, with grain boundary strengthening and dislocation strengthening being the main strengthening mechanisms of the new spring steels. It was also observed that the fine grain size and appropriate amounts of austenite made the spring steels maintain good plasticity and have ultra-high strength. Moreover, compared with the existing ultra-high strength steels at the same strength grade, the new spring steels had significant technological and cost advantages. Hence, based on the above research, a new method and theory are provided to design chemical composition and heat treatment processes for quenched and tempered steels.

Key words： spring steel ultra-high strength heat treatment machine learning

收稿日期: 2022-02-14

ZTFLH:

TG142.33

基金资助:国家自然科学基金项目(52101118);中国科协青年人才托举工程项目(2022QNRC001)

通讯作者: 赵帆，zhaofan@ustb.edu.cn，主要从事高品质特殊钢开发和质量控制研究

Corresponding author: ZHAO Fan, Tel: (010)62332253, E-mail: zhaofan@ustb.edu.cn

作者简介: 杨累，男，1995年生，硕士生

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https://www.ams.org.cn/CN/10.11900/0412.1961.2022.00047 或 https://www.ams.org.cn/CN/Y2023/V59/I11/1499

图1 机器学习设计系统(MLDS)原理图

图2 神经元网络结构图

图3 抗拉强度和断后伸长率的C2P模型训练结果

图4 MLDS和P2C模型所设计元素含量和热处理工艺参数的波动分析

图5 抗拉强度和伸长率的样本数据和MLDS设计结果

表1 MLDS预测结果和实验验证结果的比较

图6 1#钢和2#钢在不同温度淬火后的XRD谱

图7 1#钢和2#钢在不同温度淬火后显微组织的SEM像

图8 淬火温度对力学性能的影响

图9 淬火保温时间对力学性能的影响

图10 1#钢和2#钢在不同温度回火后试样的XRD谱

图11 1#钢和2#钢在不同温度回火后显微组织的SEM像

图12 1#钢和2#钢在不同温度回火后显微组织的TEM像

图13 回火温度对力学性能的影响

图14 回火时间对力学性能的影响

图15 1#钢和2#钢晶粒取向的EBSD像

图16 1#钢和2#钢碳膜萃取析出相的TEM像和EDS分析结果

图17 各种强化机理对屈服强度的贡献

图18 孪晶的TEM明场像和选区电子衍射花样

图19 1#钢和2#钢在不同温度回火后的拉伸曲线

图20 奥氏体的TEM明场像和选区电子衍射花样

图21 所开发钢种与文献[35,36]报道60Si2CrVAT的力学性能和合金元素含量对比(c) alloying element content

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