Modeling of the hot-deformation behavior of Fe-Ni-Al maraging steel using constitutive equations and machine learning algorithms
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OpenAlex topics: Metallurgy and Material Forming · Microstructure and Mechanical Properties of Steels · Metal Alloys Wear and Properties
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References (58)
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1. Raabe D, Ponge D, Dmitrieva O, Sander B (2009) Designing ultrahigh strength steels with good ductility by combining transformation induced plasticity and martensite aging. Adv Eng Mater 11:547–55510.1002/adem.200900061 ↗
2. Tewari R, Mazumder S, Batra IS, Dey GK, Banerjee S (2000) Precipitation in 18 wt% Ni maraging steel of grade 350. Acta Mater 48:1187–120010.1016/S1359-6454(99)00370-5 ↗
3. Liu G, Su J, Wang A, Yang Z, Ding Y, Ning J, Gao Q (2023) A novel Fe-Cr-Ni-Co-Mo maraging stainless steel with enhanced strength and cryogenic toughness: Role of austenite with core-shell structures. Mater Sci Eng A 863:14453710.1016/j.msea.2022.144537 ↗
4. He Y, Yang K, Qu WS, Kong FY, Su GY (2002) Strengthening and toughing of a 2800-MPa grade maraging steel. Mater Lett 56:763–76910.1016/S0167-577X(02)00610-9 ↗
5. Schnitzer R, Schober M, Zinner S, Leitner H (2010) Effect of Cu on the evolution of precipitation in an Fe-Cr-Ni-Al-Ti maraging steel. Acta Mater 58:3733–374110.1016/j.actamat.2010.03.010 ↗
6. Galindo-Nava EI, Rainforth WM, Rivera-Díaz-del-Castillo PEJ (2016) Predicting microstructure and strength of maraging steels: Elemental optimization. Acta Mater 117:270–28510.1016/j.actamat.2016.07.020 ↗
7. Tian JL, Shahzad MB, Wang W, Yin LC, Jiang Z, Yang K (2018) Role of Co in formation of Ni-Ti clusters in maraging stainless steel. J Mater Sci Technol 34:1671–167510.1016/j.jmst.2018.04.020 ↗
8. Kürnsteiner P, Wilms MB, Weisheit A, Barriobero-Vila P, Jägle EA, Raabe D (2017) Massive nanoprecipitation in an Fe-19Ni-xAl maraging steel triggered by the intrinsic heat treatment during laser metal deposition. Acta Mater 129:52–6010.1016/j.actamat.2017.02.069 ↗
9. Rao MN (2006) Progress in understanding the metallurgy of 18% nickel maraging steels. Int J Mater Res 97:1594–160710.3139/146.101418 ↗
10. Teng ZK, Ghosh G, Miller MK, Huang S, Clausen B, Brown DW, Liaw PK (2012) Neutron-diffraction study and modeling of the lattice parameters of a NiAl-precipitate-strengthened Fe-based alloy. Acta Mater 60:5362–536910.1016/j.actamat.2012.05.033 ↗
11. Jiang S, Wang H, Wu Y, Liu X, Chen H, Yao M, Gault B, Ponge D, Raabe D, Hirata A, Chen M, Wang Y, Lu Z (2017) Ultrastrong steel via minimal lattice misfit and high-density nanoprecipitation. Nature 544:460–46410.1038/nature22032 ↗
12. Samantaray D, Mandal S, Bhaduri AK (2009) A comparative study on Johnson Cook, modified Zerilli-Armstrong and Arrhenius-type constitutive models to predict elevated temperature flow behaviour in modified 9Cr-1Mo steel. Comput Mater Sci 47:568–57610.1016/j.commatsci.2009.09.025 ↗
13. Lee S, Jung J, Baik S, Park SH, Kim M, Lee Y, Euh K (2021) Effects of Ti addition on the microstructure and mechanical properties of Al-Zn-Mg-Cu-Zr alloy. Mater Sci Eng A 801:14043710.1016/j.msea.2020.140437 ↗
14. Farabi E, Zarei-Hanzaki A, Abedi HR (2016) Processing map development through elaborating phenomenological and physical constitutive based models. Adv Eng Mater 18:572–58110.1002/adem.201500296 ↗
15. Lin YC, Chen X (2011) A critical review of experimental results and constitutive descriptions for metals and alloys in hot working. Mater Des 32:1733–175910.1016/j.matdes.2010.11.048 ↗
16. Zerilli FJ, Armstrong RW (1992) The effect of dislocation drag on the stress-strain behavior of F.C.C. metals. Acta Metall Mater 40:1803–180810.1016/0956-7151(92)90166-C ↗
17. Voyiadjis GZ, Abed FH (2005) Microstructural based models for bcc and fcc metals with temperature and strain rate dependency. Mech Mater 37:355–37810.1016/j.mechmat.2004.02.003 ↗
18. Mirzaie T, Mirzadeh H, Cabrera J (2016) A simple Zerilli-Armstrong constitutive equation for modeling and prediction of hot deformation flow stress of steels. Mech Mater 94:38–4510.1016/j.mechmat.2015.11.013 ↗
19. Sellars CM, McTegart WJ (1966) On the mechanism of hot deformation. Acta Metall 14:1136–113810.1016/0001-6160(66)90207-0 ↗
20. Babu KA, Mandal S (2017) Regression based novel constitutive analyses to predict high temperature flow behavior in super austenitic stainless steel. Mater Sci Eng A 703:187–19510.1016/j.msea.2017.07.035 ↗
21. Mei RB, Bao L, Cai B, Liu X (2016) Piecewise modeling of flow stress of 7075–T6 aluminum alloy in hot deformation. Mater Trans 57:1147–115510.2320/matertrans.M2015465 ↗
22. Mei R, Bao L, Huang F, Zhang X, Qi X, Liu X (2018) Simulation of the flow behavior of AZ91 magnesium alloys at high deformation temperatures using a piecewise function of constitutive equations. Mech Mater 125:110–12010.1016/j.mechmat.2018.07.011 ↗
23. Huo Y, He T, Chen S, Ji H, Wu R (2019) Microstructure evolution and unified constitutive equations for the elevated temperature deformation of SAE 52100 bearing steel. J Manuf Process 44:113–12410.1016/j.jmapro.2019.05.051 ↗
24. Li H, Hu J, Wei D, Wang X, Li Y (2012) Artificial neural network and constitutive equations to predict the hot deformation behavior of modified 2.25Cr–1Mo steel. Mater Des 42:192–19710.1016/j.matdes.2012.05.056 ↗
25. Liu X, Xu P, Zhao J, Lu W, Li M, Wang G (2022) Material machine learning for alloys: applications, challenges and perspectives. J Alloys Compd 921:16598410.1016/j.jallcom.2022.165984 ↗
26. Ramprasad R, Batra R, Pilania G, Mannodi-Kanakkithodi A, Kim C (2017) Machine learning in materials informatics: recent applications and prospects. npj Comput Mater 3(1):54. https://doi.org/10.1038/s41524-017-0056-510.1038/s41524-017-0056-5 ↗
27. Kumar S, Pradhan H, Shah N, Rahul MR, Gandham P (2023) Machine learning enabled processing map generation for high-entropy alloy. Scr Mater 234:11554310.1016/j.scriptamat.2023.115543 ↗
28. Qiao L, Zhu J (2022) Constitutive modeling of hot deformation behavior of AlCrFeNi multi-component alloy. Vacuum 201:11105910.1016/j.vacuum.2022.111059 ↗
29. Xiong J, He J, Leng X, Zhang T (2023) Gaussian process regressions on hot deformation behaviors of FGH98 nickel-based powder superalloy. J Mater Sci Technol 146:177–18510.1016/j.jmst.2022.10.063 ↗
30. Decke J, Engelhardt A, Rauch L, Degener S, Sajadifar SV, Scharifi E, Steinhoff K, Niendorf T, Sick B (2022) Predicting flow stress behavior of an AA7075 alloy using machine learning methods. Crystals 12:128110.3390/cryst12091281 ↗
31. Katakareddi G, Ali MS, Jungalwala K, Yedla N (2024) Predicting stress–strain characteristics of hot deformed Cu-Zr metallic glass alloy composite nanowires using supervised machine learning algorithms. J Mater Eng Perform. https://doi.org/10.1007/s11665-024-09739-210.1007/s11665-024-09739-2 ↗
32. Qiao L, Deng Y, Liao M, Zhu J (2020) Modelling and prediction of thermal deformation behaviors in a pearlitic steel. Mater Today Commun 25:10113410.1016/j.mtcomm.2020.101134 ↗
33. Qiao L, Inoue J, Zhu J (2024) Machine learning guided constitutive model and processing map for Fe2Ni2CrAl1.2 multi-principle element alloys. J Mater Res Technol 29:353–36310.1016/j.jmrt.2024.01.119 ↗
34. Hamada A, Khosravifard A, Ghosh S, Jaskari M, Järvenpää A, Karjalainen P (2022) High-speed erichsen testing of grain-refined 301LN austenitic stainless steel processed by double-reversion annealing. Metall Mater Trans A 53:2174–219410.1007/s11661-022-06659-5 ↗
35. Kim WJ, Jeong HT (2019) Pronounced yield drop phenomenon at high temperatures in Al-Mg alloys with high contents of Mg (5–13 wt%). Mater Sci Eng A 743:590–59610.1016/j.msea.2018.11.102 ↗
36. Samantaray D, Mandal S, Borah U, Bhaduri AK, Sivaprasad PV (2009) A thermo-viscoplastic constitutive model to predict elevated-temperature flow behaviour in a titanium-modified austenitic stainless steel. Mater Sci Eng A 526:1–610.1016/j.msea.2009.08.009 ↗
37. Zener C, Hollomon JH (1944) Effect of strain rate upon plastic flow of steel. J Appl Phys 15:22–3210.1063/1.1707363 ↗
38. Tomiczek B, Snopiński P, Borek W, Król M, Gutiérrez AR, Matula G (2023) Hot deformation behaviour of additively manufactured 18Ni-300 maraging steel. Materials 16:241210.3390/ma16062412 ↗
39. Chakravarthi KVA, Koundinya NTBN, Narayana Murty SVS, Nageswara Rao B (2017) Microstructural evolution and constitutive relationship of M350 grade maraging steel during hot deformation. J Mater Eng Perform 26:1174–118510.1007/s11665-017-2539-4 ↗
40. Chakravarthi KVA, Koundinya NTBN, Narayana Murty SVS, Nageswara Rao B (2018) Optimization of hot workability and control of microstructure in CF250 grade cobalt-free maraging steel: an approach using processing maps. Metallogr Microstruct Anal 7:35–4710.1007/s13632-017-0408-z ↗
41. Zhang L, Wang W, Babar Shahzad M, Shan YY, Yang K (2019) Hot deformation behavior of an ultra-high-strength Fe–Ni–Co-based maraging steel. Acta Metall Sin (Engl Lett) 32:1161–117210.1007/s40195-019-00913-3 ↗
42. Breiman L (2001) Random forests. Mach Learn 45:5–3210.1023/A:1010933404324 ↗
43. Sigrist F (2021) Gradient and Newton boosting for classification and regression. Expert Syst Appl 167:11408010.1016/j.eswa.2020.114080 ↗
44. Chen T, Guestrin C (2016) XGBoost: a scalable tree boosting system. In: Proceedings of the 22nd ACM SIGKDD Conference on knowledge discovery and data mining (KDD) p 785–79410.1145/2939672.2939785 ↗
45. Schlölkopf B, Smola AJ (2004) A tutorial on support vector regression. Stat Comput 14:199–22210.1023/B:STCO.0000035301.49549.88 ↗
46. Hoerl AE, Kennard RW (1970) Ridge regression: Biased estimation for nonorthogonal problems. Technometrics 12:55–6710.1080/00401706.1970.10488634 ↗
47. Breiman L, Friedman JH, Olshen RA, Stone CJ (1984) Classification and regression trees. CRC, Boca Raton
48. Xiang S, Chen X, Fan Z, Chen T, Lian X (2022) A deep learning-aided prediction approach for creep rupture time of Fe-Cr-Ni heat-resistant alloys by integrating textual and visual features. J Mater Res Technol 18:268–28110.1016/j.jmrt.2022.02.099 ↗
49. Cheng CL, Shalabh GG (2014) Coefficient of determination for multiple measurement error models. J Multivar Anal 126:137–15210.1016/j.jmva.2014.01.006 ↗
50. Onyutha C (2022) A hydrological model skill score and revised R-squared. Hydrol Res 53:51–6410.2166/nh.2021.071 ↗
51. Binali R (2024) Experimental and machine learning comparison for measurement the machinability of nickel based alloy in pursuit of sustainability. Measurement 236:11514210.1016/j.measurement.2024.115142 ↗
52. Gong C, Liu J, Dai S, Hao H, Liu H (2024) Machine learning assisted prediction of the phonon cutoff frequency of ABO3 perovskite materials. Comput Mater Sci 239:11294310.1016/j.commatsci.2024.112943 ↗
53. Xie Q, Suvarna M, Li J, Zhu X, Cai J, Wang X (2021) Online prediction of mechanical properties of hot rolled steel plate using machine learning. Mater Des 197:10920110.1016/j.matdes.2020.109201 ↗
54. Li W, Liang Y, Liu Y (2022) Failure load prediction and optimisation for adhesively bonded joints enabled by deep learning and fruit fly optimization. Adv Eng Inform 54:10181710.1016/j.aei.2022.101817 ↗
55. Prasad YVRK, Gegel HL, Doraivelu SM, Malas JC, Morgan JT, Lark KA, Barker DR (1984) Modeling of dynamic material behavior in hot deformation: forging of Ti-6242. Metall Trans A 15:1883–189210.1007/BF02664902 ↗
56. Wu H, Wen SP, Huang H, Wu XL, Gao KY, Wang W (2015) Hot deformation behavior and constitutive equation of a new type Al-Zn-Mg-Er-Zr alloy during isothermal compression. Mater Sci Eng A 651:415–42410.1016/j.msea.2015.10.122 ↗
57. Niu Y, Hou J, Ning F, Chen X, Jia Y, Le Q (2020) Hot deformation behavior and processing map of Mg-2Zn-1Al-0.2RE alloy. J Rare Earths 38:665–67510.1016/j.jre.2019.09.007 ↗
58. Chen X, Liao Q, Niu Y, Jia Y, Le Q, Ning S, Hu C, Hu K, Yu F (2019) Comparison study of hot deformation behavior and processing map of AZ80 magnesium alloy casted with and without ultrasonic vibration. J Alloy Compd 803:585–59610.1016/j.jallcom.2019.06.242 ↗