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

doi:10.11900/0412.1961.2022.00047

Acta Metall Sin

2023, Vol. 59

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

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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

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YANG Lei, ZHAO Fan, JIANG Lei, XIE Jianxin. Development of Composition and Heat Treatment Process of 2000 MPa Grade Spring Steels Assisted by Machine Learning. Acta Metall Sin, 2023, 59(11): 1499-1512.

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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

Received: 14 February 2022

ZTFLH:

TG142.33

Fund: National Natural Science Foundation of China(52101118);Young Elite Scientists Sponsorship Program by China Association for Science and Technology(2022QNRC001)

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

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https://www.ams.org.cn/EN/10.11900/0412.1961.2022.00047 OR https://www.ams.org.cn/EN/Y2023/V59/I11/1499

Fig.1 Schematic of machine learning design system (MLDS) (R_m—tensile strength, A—elongation after fracture)

Fig.2 Structure diagrams of neuron networks (t_T—tempering time)
(a) P2C model (b) C2P model

Fig.3 Training results of the C2P model (R—Pearson correlation coefficient)
(a) tensile strength (b) elongation after fracture

Fig.4 Fluctuation analyses of design results by the MLDS and the P2C model
(a) Si content (b) Mn content (c) Cr content (d) Mo content
(e) quenching temperature (f) quenching time (g) tempering temperature (h) tempering time

Fig.5 Tensile strength and elongation of sample data and design results obtained by the MLDS
(a) all data (b) magnification of design results area for rectangle zone in Fig.5a

Table 1 Comparisons of predicted results by MLDS and experimental results

Fig.6 XRD spectra of samples after quenching at different temperatures
(a) 1# steel (b) 2# steel

Fig.7 SEM images of 1# steel (a-c) and 2# steel (d-f) after quenching at 870^oC (a, d), 910^oC (b, e), and 950^oC (c, f) (Insets show the enlarged images)

Fig.8 Effects of quenching temperature on mechanical properties
(a) tensile and yield strengths (b) elongation after fracture and reduction in area (c) prediction results of the C2P model

Fig.9 Effects of holding time before quenching on mechanical properties
(a) tensile and yield strengths (b) elongation after fracture and reduction in area (c) prediction results of the C2P model

Fig.10 XRD spectra of samples after tempering at different temperatures
(a) 1# steel (b) 2# steel

Fig.11 SEM images of 1# steel (a-c) and 2# steel (d-f) after tempering at 380^oC (a, d), 420^oC (b, e), and 460^oC (c, f)

Fig.12 TEM images of 1# steel (a-c) and 2# steel (d-f) after tempering at 380^oC (a, d), 420^oC (b, e), and 460^oC (c, f)

Fig.13 Effects of tempering temperature on mechanical properties
(a) tensile and yield strengths
(b) elongation at break and reduction in area
(c) prediction results of the C2P model

Fig.14 Effects of tempering time on mechanical properties
(a) tensile and yield strengths
(b) elongation after fracture and reduction in area
(c) prediction results of the C2P model

Fig.15 (a) 1# steel (b) 2# steel
EBSD grain orientation images

Fig.16 TEM images (a, b) and EDS analysis results (c, d) of the precipitates extracted by carbon film for 1# steel (a, c) and 2# steel (b, d)

Fig.17 Contributions of various strengthening mechanisms to yield strength (σ_ss—solid solution strength, σ_ds—dislocation strength, σ_gs—grain boundary strength, σ_ps—precipitation strength, σ_y—yield strength)

Fig.18 TEM bright field image and selected area electron diffraction pattern (inset) of the twin

Fig.19 Tensile curves of samples with different tempering temperatures
(a) 1# steel (b) 2# steel

Fig.20 TEM bright field image and selected area electron diffraction pattern (inset) of austenite indicated by arrows

Fig.21 Comparisons of mechanical properties and alloying element content between the developed steels and 60Si2CrVAT reported in Refs.[35,36]
(a) tensile strength and elongation after fracture
(b) yield strength and elongation after fracture

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