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Research Article

Optimisation of hardness profiles in high-speed train axlebox bearings

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Pages 2318-2333 | Received 22 Dec 2022, Accepted 30 Mar 2023, Published online: 20 Apr 2023

References

  • Lee SC, Ho WY. The effects of surface hardening on fracture-toughness of carburized steel. Metall Trans A. 1989;20A:519–525.
  • Farfan S, Rubio-González C, Cervantes-Hernández T, et al. High cycle fatigue, low cycle fatigue and failure modes of a carburized steel. Int J Fatigue. 2004;26(6):673–678.
  • Bhadeshia HKDH. Steels for bearings. Prog Mater Sci. 2012;57(2):268–435.
  • Xie L, Palmer D, Otto F, et al. Effect of surface hardening technique and case depth on rolling contact fatigue behavior of alloy steels. Tribol T. 2014;58(2):215–224.
  • Shen Y, Moghadam SM, Sadeghi F, et al. Effect of retained austenite – compressive residual stresses on rolling contact fatigue life of carburized AISI 8620 steel. Int J Fatigue. 2015;75:135–144.
  • Genel K, Demirkol M. Effect of case depth on fatigue performance of AISI 8620 carburized steel. Int J Fatigue. 1999;21:207–212.
  • Asi O, Can AÇ, Pineault J, et al. The relationship between case depth and bending fatigue strength of gas carburized SAE 8620 steel. Surf Coat Tech. 2007;201(12):5979–5987.
  • Asi O, Can AÇ, Pineault J, et al. The effect of high temperature gas carburizing on bending fatigue strength of SAE 8620 steel. Mater Design. 2009;30(5):1792–1797.
  • Xiao N, Hui W, Zhang Y, et al. High-cycle fatigue behavior of vacuum-carburized 20Cr2Ni4 steel with different case depths. J Mater Eng Perform. 2019;28(6):3413–3422.
  • Qin S, Zhang C, Zhang B, et al. Effect of carburizing process on high cycle fatigue behavior of 18CrNiMo7-6 steel. J Mater Res Technol. 2022;16:1136–1149.
  • Roy S, Sundararajan S. Effect of retained austenite on spalling behavior of carburized AISI 8620 steel under boundary lubrication. Int J Fatigue. 2019;119:238–246.
  • Morris D, Sadeghi F. Retained austenite stability on rolling contact fatigue performance of 8620 case-carburized steel. Fatigue Fract Eng M. 2021;45(1):55–68.
  • Ooi GTC, Roy S, Sundararajan S. Investigating the effect of retained austenite and residual stress on rolling contact fatigue of carburized steel with XFEM and experimental approaches. Mat Sci Eng A-Struct. 2018;732:311–319.
  • Wei P, Zhou H, Liu H, et al. Modeling of contact fatigue damage behavior of a wind turbine carburized gear considering its mechanical properties and microstructure gradients. Int J Mech Sci. 2019;156:283–296.
  • Da Silva AD, Pedrosa TA, Gonzalez-Mendez JL, et al. Distortion in quenching an AISI 4140 C-ring - predictions and experiments. Mater Design. 2012;42:55–61.
  • Li J, Tang L, Li S, et al. Finite element simulation of deep cryogenic treatment incorporating transformation kinetics. Mater Design. 2013;47:653–666.
  • Liu Y, Qin S, Zhang J, et al. Influence of transformation plasticity on the distribution of internal stress in three water-quenched cylinders. Metall Mater Trans A. 2017;48(10):4943–4956.
  • Wang X, Li B, Gu M. Simulation analysis on martempering in salt bath technology for carburized distortion sample. Metall Mater Trans A. 2019;50(8):3758–3766.
  • Chen X, Zhang S, Rolfe B, et al. The FEM simulation and experiment of quenching distortion of a U-shape sample and the sensitivity analysis of material properties. Mater Res Express. 2019;6(11):116539–116552.
  • Xu L, Xing J, Wei S, et al. Optimization of heat treatment technique of high-vanadium high-speed steel based on back-propagation neural networks. Mater Design. 2007;28(5):1425–1432.
  • Ji C, Hu J, Wang B, et al. Mechanical behavior prediction of CF/PEEK-titanium hybrid laminates considering temperature effect by artificial neural network. Compos Struct. 2021;262:1–13.
  • Zhang J, Huang Y, Ma G, et al. Mixture optimization for environmental, economical and mechanical objectives in silica fume concrete: A novel frame-work based on machine learning and a new meta-heuristic algorithm. Resour Conserv Recy. 2021;167:1–21.
  • Chen Y, Chen S, Wu Z, et al. Optimization of genetic algorithm through use of back propagation neural network in forecasting smooth wall blasting parameters. Mathematics-Basel. 2022;10(8):1–21.
  • Powar A, Date P. Modeling of microstructure and mechanical properties of heat treated components by using artificial neural network. Mat Sci Eng A-Struct. 2015;628:89–97.
  • Podgornik B, Belič I, Leskovšek V, et al. Tool steel heat treatment optimization using neural network modeling. Metall Mater Trans A. 2016;47(11):5650–5659.
  • Babu K K, Panneerselvam K, Sathiya P, et al. Parameter optimization of friction stir welding of cryorolled AA2219 alloy using artificial neural network modeling with genetic algorithm. Int J Adv Manuf Technol. 2017;94(9-12):3117–3129.
  • Liang R, Wang Z, Yang S, et al. Study on hardness prediction and parameter optimization for carburizing and quenching: an approach based on FEM, ANN and GA. Mater Res Express. 2021;8(11):1–13.
  • Cao Z, Shi Z, Yu F, et al. Effects of double quenching on fatigue properties of high carbon bearing steel with extra-high purity. Int J Fatigue. 2019;128:1–6.
  • Hornik K, Stinchcombe M, White H. Multilayer feedforward networks are universal approximators. Neural Netw. 1989 1989;2(5):359–366.
  • Guo Y, Wang C, Ma Z, et al. A new mesh smoothing method based on a neural network. Comput Mech. 2021;69(2):425–438.
  • Katoch S, Chauhan SS, Kumar V. A review on genetic algorithm: past, present, and future. Multimed Tools Appl. 2020;80(5):8091–8126.
  • Lee S-J, Matlock DK, Van Tyne CJ. An empirical model for carbon diffusion in austenite incorporating alloying element effects. ISIJ Int. 2011 2011;51(11):1903–1911.
  • Zhang X, Tang J-y, Zhang X-r. An optimized hardness model for carburizing-quenching of low carbon alloy steel. J Cent South Univ. 2017;24(1):9–16.
  • Liu Z. Numerical simulation of heat treatment process in 18CrNiMo7-6 gear and determination of heat transfer coefficient [master’s thesis]. Dalian (LN): Dalian jiaotong university; 2013. China.
  • Gergely M, Somogyi S, Réti T, et al. Computerized properties prediction and technology planning in heat treatment of steels. ASM Int. 1991;4:638–657.
  • Maynier P, Dollet J, Bastien P. Prediction of microstructure via empirical formulas based on CCT diagrams. Metall Soc AIME. 1978: 163–178.
  • Zhang Y. Numerical simulation and experimental study on carburizing-quenching process of 18CrNiMo7-6 alloy steel [master’s thesis]. Zhengzhou (HN): Zhengzhou University; 2019. China.
  • Zhu Z, Liang Y, Yin C, et al. Influence of friction-induced retained austenite transformation to martensite on the wear properties of a carburized layer of 23CrNi3MoA steel. Appl Surf Sci. 2022;595:1–14.
  • Guo J, Deng X, Wang H, et al. Modeling and simulation of vacuum low pressure carburizing process in gear steel. Coatings. 2021;11(8):1–15.

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