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
Cite this article:
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.
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 950oC for 30 min and oil quenching + tempering at 380oC 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.
Fund: National Natural Science Foundation of China(52101118);Young Elite Scientists Sponsorship Program by China Association for Science and Technology(2022QNRC001)
Fig.1 Schematic of machine learning design system (MLDS) (Rm—tensile strength, A—elongation after fracture)
Fig.2 Structure diagrams of neuron networks (tT—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
Steel
Chemical composition (mass fraction / %)
Heat treatment parameter
Mechanical property
C
Si
Mn
Cr
Ni
Mo
V
Nb
TQ
tQ
TT
tT
Rm
A
Rp0.2
Z
oC
min
oC
min
MPa
%
MPa
%
1# predicted
0.50
1.63
0.73
1.20
0.21
0.27
0.14
0.020
907
31
423
89
2075
12.0
-
-
1# actual
0.55
1.76
0.70
1.10
0.21
0.20
0.14
0.016
910
30
420
90
2046
11.9
1644
38.0
2# predicted
0.57
1.70
0.70
1.17
0.18
0.22
0.34
0.010
914
33
420
93
2096
10.9
-
-
2# actual
0.54
1.75
0.64
1.18
0.20
0.20
0.37
0.003
910
30
420
90
2044
10.0
1695
33.9
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 870oC (a, d), 910oC (b, e), and 950oC (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 380oC (a, d), 420oC (b, e), and 460oC (c, f)
Fig.12 TEM images of 1# steel (a-c) and 2# steel (d-f) after tempering at 380oC (a, d), 420oC (b, e), and 460oC (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.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 (σss—solid solution strength, σds—dislocation strength, σgs—grain boundary strength, σps—precipitation strength, σy—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
1
Ou M G, Yang C L, Zhu J, et al. Influence of Cr content and Q-P-T process on the microstructure and properties of cold-coiled spring steel [J]. J. Alloys Compd., 2017, 697: 43
doi: 10.1016/j.jallcom.2016.12.134
2
Nam W J, Kim D S, Ahn S T. Effects of alloying elements on microstructural evolution and mechanical properties of induction quenched-and-tempered steels [J]. J. Mater. Sci., 2003, 38: 3611
doi: 10.1023/A:1025625330442
3
Wu D, Wang F M, Cheng J, et al. Effects of Nb and tempering time on carbide precipitation behavior and mechanical properties of Cr-Mo-V steel for brake discs [J]. Steel Res. Int., 2018, 89: 1700491
doi: 10.1002/srin.v89.5
4
Zhang J Z, Dai Z B, Zeng L Y, et al. Revealing carbide precipitation effects and their mechanisms during quenching-partitioning-tempering of a high carbon steel: Experiments and modeling [J]. Acta Mater., 2021, 217: 117176
doi: 10.1016/j.actamat.2021.117176
5
Zurnadzhy V I, Efremenko V G, Wu K M, et al. Effects of stress relief tempering on microstructure and tensile/impact behavior of quenched and partitioned commercial spring steel [J]. Mater. Sci. Eng., 2019, A745: 307
6
Jiang B, Wu M, Zhang M, et al. Microstructural characterization, strengthening and toughening mechanisms of a quenched and tempered steel: Effect of heat treatment parameters [J]. Mater. Sci. Eng., 2017, A707: 306
7
Xu L, Chen L, Sun W Y. Effects of soaking and tempering temperature on microstructure and mechanical properties of 65Si2MnWE spring steel [J]. Vacuum, 2018, 154: 322
doi: 10.1016/j.vacuum.2018.05.029
8
Kim S H, Kim K H, Bae C M, et al. Microstructure and mechanical properties of austempered medium-carbon spring steel [J]. Met. Mater. Int., 2018, 24: 693
doi: 10.1007/s12540-018-0085-8
9
Jiang B, Mei Z, Zhou L Y, et al. Microstructure evolution, fracture and hardening mechanisms of quenched and tempered steel for large sized bearing rings at elevated quenching temperatures [J]. Met. Mater. Int., 2016, 22: 572
doi: 10.1007/s12540-016-5493-z
10
Barani A A, Ponge D, Raabe D. Refinement of grain boundary carbides in a Si-Cr spring steel by thermomechanical treatment [J]. Mater. Sci. Eng., 2006, A426: 194
11
Su Y J, Fu H D, Bai Y, et al. Progress in materials genome engineering in China [J]. Acta Metall. Sin., 2020, 56: 1313
Xie J X, Su Y J, Xue D Z, et al. Machine learning for materials research and development [J]. Acta Metall. Sin., 2021, 57: 1343
doi: 10.11900/0412.1961.2021.00357
Wang C S, Fu H D, Jiang L, et al. A property-oriented design strategy for high performance copper alloys via machine learning [J]. npj Comput. Mater., 2019, 5: 87
doi: 10.1038/s41524-019-0227-7
14
Shen C G, Wang C C, Wei X L, et al. Physical metallurgy-guided machine learning and artificial intelligent design of ultrahigh-strength stainless steel [J]. Acta Mater., 2019, 179: 201
doi: 10.1016/j.actamat.2019.08.033
15
Jiang L, Wang C S, Fu H D, et al. Discovery of aluminum alloys with ultra-strength and high-toughness via a property-oriented design strategy [J]. J. Mater. Sci. Technol., 2021, 98: 33
doi: 10.1016/j.jmst.2021.05.011
16
Schoenholz S S, Cubuk E D, Sussman D M, et al. A structural approach to relaxation in glassy liquids [J]. Nat. Phys., 2016, 12: 469
doi: 10.1038/NPHYS3644
17
Cubuk E D, Schoenholz S S, Rieser J M, et al. Identifying structural flow defects in disordered solids using machine-learning methods [J]. Phys. Rev. Lett., 2015, 114: 108001
doi: 10.1103/PhysRevLett.114.108001
18
Lu S H, Zhou Q H, Ouyang Y X, et al. Accelerated discovery of stable lead-free hybrid organic-inorganic perovskites via machine learning [J]. Nat. Commun., 2018, 9: 3405
doi: 10.1038/s41467-018-05761-w
pmid: 30143621
19
Xu Q C, Li Z Z, Liu M, et al. Rationalizing perovskite data for machine learning and materials design [J]. J. Phys. Chem. Lett., 2018, 9: 6948
doi: 10.1021/acs.jpclett.8b03232
pmid: 30481460
20
Fleischer R L. Substitutional solution hardening [J]. Acta Metall., 1963, 11: 203
doi: 10.1016/0001-6160(63)90213-X
21
Toda-Caraballo I, Galindo-Nava E I, Rivera-Díaz-Del-Castillo P E J. Understanding the factors influencing yield strength on Mg alloys [J]. Acta Mater., 2014, 75: 287
doi: 10.1016/j.actamat.2014.04.064
22
Galindo-Nava E I, Rivera-Díaz-Del-Castillo P E J. A model for the microstructure behaviour and strength evolution in lath martensite [J]. Acta Mater., 2015, 98: 81
doi: 10.1016/j.actamat.2015.07.018
23
Youssef K M, Scattergood R O, Murty K L, et al. Nanocrystalline Al-Mg alloy with ultrahigh strength and good ductility [J]. Scr Mater., 2006, 54: 251
doi: 10.1016/j.scriptamat.2005.09.028
24
Zhao Y H, Liao X Z, Jin Z, et al. Microstructures and mechanical properties of ultrafine grained 7075 Al alloy processed by ECAP and their evolutions during annealing [J]. Acta Mater., 2004, 52: 4589
doi: 10.1016/j.actamat.2004.06.017
25
Zhu J, Xie J X, Zhang Z H, et al. Microstructure and obdurability improvement mechanisms of the La-microalloyed H13 steel [J]. Steel Res. Int., 2018, 89: 1800044
doi: 10.1002/srin.v89.12
26
Krasilnikov N, Lojkowski W, Pakiela Z, et al. Tensile strength and ductility of ultra-fine-grained nickel processed by severe plastic deformation [J]. Mater. Sci. Eng., 2005, A397: 330
27
Kako K, Kawakami E, Ohta J, et al. Effects of various alloying elements on tensile properties of high-purity Fe-18Cr-(14-16)Ni allo-ys at room temperature [J]. Mater. Trans., 2002, 43: 155
doi: 10.2320/matertrans.43.155
28
Gladman T. Precipitation hardening in metals [J]. Mater. Sci. Technol., 1999, 15: 30
doi: 10.1179/026708399773002782
29
Shi R J, Wang Z D, Qiao L J, et al. Microstructure evolution of in-situ nanoparticles and its comprehensive effect on high strength steel [J]. J. Mater. Sci. Technol., 2019, 35: 1940
doi: 10.1016/j.jmst.2019.05.009
30
Rivera-Díaz-Del-Castillo P E J, Hayashi K, Galindo-Nava E I. Computational design of nanostructured steels employing irreversible thermodynamics [J]. Mater. Sci. Technol., 2013, 29: 1206
doi: 10.1179/1743284712Y.0000000179
31
Jo M C, Choi J H, Lee H, et al. Effects of solute segregation on tensile properties and serration behavior in ultra-high-strength high-Mn TRIP steels [J]. Mater. Sci. Eng., 2019, A740-741: 16
32
Hamada A S, Karjalainen L P, Somani M C. The influence of aluminum on hot deformation behavior and tensile properties of high-Mn TWIP steels [J]. Mater. Sci. Eng., 2007, A467: 114
33
Zhang H L, Sun M Y, Liu Y X, et al. Ultrafine-grained dual-phase maraging steel with high strength and excellent cryogenic toughness [J]. Acta Mater., 2021, 211: 116878
doi: 10.1016/j.actamat.2021.116878
34
Xu Y T, Li W, Du H, et al. Tailoring the metastable reversed austenite from metastable Mn-rich carbides [J]. Acta Mater., 2021, 214: 116986
doi: 10.1016/j.actamat.2021.116986
35
Jiang Y, Zou J H, Liang Y L, et al. Influence of carbides on the strain hardening behavior of 60Si2CrVAT spring steel treated by a Q&T process [J]. Mater. Sci. Eng., 2021, A823: 141695
36
Wu H L, Wang F M, Li C R, et al. Optimization of heat treatment process of 60Si2CrVAT spring steel for high-speed trains [J]. Trans. Mater. Heat Treat., 2011, 32(9): 35
Jiang S H, Wang H, Wu Y, et al. Ultrastrong steel via minimal lattice misfit and high-density nanoprecipitation [J]. Nature, 2017, 544: 460
doi: 10.1038/nature22032
38
Pardal J M, Tavares S S M, Terra V F, et al. Modeling of precipitation hardening during the aging and overaging of 18Ni-Co-Mo-Ti maraging 300 steel [J]. J. Alloys Compd., 2005, 393: 109
doi: 10.1016/j.jallcom.2004.09.049
39
Niu M C, Zhou G, Wang W, et al. Precipitate evolution and strengthening behavior during aging process in a 2.5 GPa grade maraging steel [J]. Acta Mater., 2019, 179: 296
doi: 10.1016/j.actamat.2019.08.042
40
Kim Y K, Kim K S, Song Y B, et al. 2.47 GPa grade ultra-strong 15Co-12Ni secondary hardening steel with superior ductility and fracture toughness [J]. J. Mater. Sci. Technol., 2021, 66: 36
doi: 10.1016/j.jmst.2020.06.014
41
Tharian K T, Sivakumar D, Ganesan R, et al. Development of new low nickel, cobalt free maraging steel [J]. Mater. Sci. Technol., 1991, 7: 1082
doi: 10.1179/mst.1991.7.12.1082
42
Zhang J Z, Cui Y G, Zuo X W, et al. Dislocations across interphase enable plain steel with high strength-ductility [J]. Sci. Bull., 2021, 66: 1058
doi: 10.1016/j.scib.2021.02.032
pmid: 36654339
43
Viswanathan U K, Kishore R, Asundi M K. Effect of thermal cycling on the mechanical properties of 350-grade maraging steel [J]. Metall. Mater. Trans., 1996, 27A: 757
