Integrated shape-morphing and metamodel-based optimization of railway wheel web considering thermo-mechanical loads

  • Soyoung Lee
  • Dong Hyung Lee
  • Jongsoo Lee
Industrial Application


While the wheel and the rail are in contact, the stress on the wheel affects the safety of the vehicle and causes ride discomfort and noise. Therefore, the design of the wheel to reduce the damage is needed. To solve these problems, the shape optimization on the railway wheel web was carried out using the finite-element analysis and shape-morphing technique. First, thermo-mechanical analysis, with consideration to mechanical and thermal loads under tread braking, was developed and the contact pressure and stress generated on the wheel were obtained. Then, the integrated shape-morphing design process was proposed to improve the efficiency of the shape optimization. It made it possible to parameterize the finite-element models and modify them directly without returning to the computer-aided design model. Based on the analysis results, we performed the metamodel and genetic algorithm based optimization by using response surface method, Kriging and Gaussian process, then compared their optimal solutions. Optimization proceeded in two stages, two-dimensional optimization under axisymmetric conditions and three-dimensional optimization in which the shape changed in the period of 60°. For the post-optimization work, fatigue evaluation for the verifications on initial and optimal wheel models was performed and the results were discussed.


Railway wheel web Thermo-mechanical analysis Morphing Integrated shape-morphing Metamodel-based optimization 



This research was supported by the Korea Railway Research Institute (2016-11-0776, 2016-11-1750). This research was also supported by the Basic Science Research Program through the National Research Foundation of Korea (NRF), funded by the Ministry of Science, ICT and Future Planning (2017R1A2B4009606).


  1. ABAQUS 6.12 User’s Manual, Version 6.12 (2012) Dassault Systemes Simulia Inc., ProvidenceGoogle Scholar
  2. Ahn JG, You ID, Kwon SJ, Kim HK (2012) Estimation of contact fatigue initiation lifetime of an urban railway wheel. J Korean Soc Tribol Lubr Eng 28(1):19–26Google Scholar
  3. Arslan MA, Kayabaşı O (2012) 3-D rail–wheel contact analysis using FEA. Adv Eng Softw 45(1):325–331CrossRefGoogle Scholar
  4. Choi HY, Lee DH, Lee J (2013) Optimization of a railway wheel profile to minimize flange wear and surface fatigue. Wear 300(1–2):225–233CrossRefGoogle Scholar
  5. Fermér M (1994) Optimization of a railway freight car wheel by use of a fractional factorial design method. Proc Inst Mech Eng F 208(2):97–107CrossRefGoogle Scholar
  6. Flager F, Welle B, Bansal P, Soremekun G, Haymaker J (2009) Multidisciplinary process integration and design optimization of a classroom building. J Inform Technol Constr 14(14):595–612Google Scholar
  7. Hamri O, Léon JC, Giannini F, Falcidieno B (2010) Software environment for CAD/CAE integration. Adv Eng Softw 41(10–11):1211–1222CrossRefzbMATHGoogle Scholar
  8. Handa K, Morimoto F (2012) Influence of wheel/rail tangential traction force on thermal cracking of railway wheels. Wear 289:112–118CrossRefGoogle Scholar
  9. Hyndman RJ, Koehler AB (2006) Another look at measures of forecast accuracy. Int J Forecast 22(4):679–688CrossRefGoogle Scholar
  10. Hyperworks User’s Manual (2012) Altair Engineering Inc., Troy, MIGoogle Scholar
  11. Jarvis RM, Goodacre R (2004) Genetic algorithm optimization for pre-processing and variable selection of spectroscopic data. Bioinformatics 21(7):860–868CrossRefGoogle Scholar
  12. Jin R, Chen W, Simpson TW (2001) Comparative studies of metamodelling techniques under multiple modelling criteria. Struct Multidiscip Optim 23(1):1–13CrossRefGoogle Scholar
  13. Kaya N, Karen I, Öztürk F (2010) Re-design of a failed clutch fork using topology and shape optimization by the response surface method. Mater Des 31(6):3008–3014CrossRefGoogle Scholar
  14. Krishnakumar K (1990) Micro-genetic algorithms for stationary and non-stationary function optimization. In intelligent control and adaptive systems. Int Soc Optics Photon 1196:289–297Google Scholar
  15. Lee JS, Kim SC (2007) Optimal design of engine mount rubber considering stiffness and fatigue strength. Proc Inst Mech Eng D 221(7):823–835CrossRefGoogle Scholar
  16. Lee G, Park J, Choi DH (2012a) Shape optimization of mobile phone folder module for structural strength. J Mech Sci Technol 26(2):509–515CrossRefGoogle Scholar
  17. Lee GS, Park JM, Choi BL, Choi DH, Nam CH, Kim GH (2012b) Multidisciplinary design optimization of vehicle front suspension system using PIDO technology. Transactions of KSAE 20(6):1–8CrossRefGoogle Scholar
  18. Lei F, Qiu R, Bai Y, Yuan C (2018) An integrated optimization for laminate design and manufacturing of a CFRP wheel hub based on structural performance. Struct Multidiscip Optim 57(6):2309–2321CrossRefGoogle Scholar
  19. Liu W, Yang Y (2007) Multi-objective optimization of an auto panel drawing die face design by mesh morphing. Comput Aided Des 39(10):863–869CrossRefGoogle Scholar
  20. Liu Y, Liu L, Mahadevan S (2007) Analysis of subsurface crack propagation under rolling contact loading in railroad wheels using FEM. Eng Fract Mech 74(17):2659–2674CrossRefGoogle Scholar
  21. Lundén R (1991) Contact region fatigue of railway wheels under combined mechanical rolling pressure and thermal brake loading. Wear 144(1–2):57–70CrossRefGoogle Scholar
  22. MATLAB User’s Guide, R2018a (2018), Mathworks, Natick, MAGoogle Scholar
  23. Myers RH, Montgomery DC (1995) Response surface methodology—process and product optimization using designed experiments. Wiley, New YorkzbMATHGoogle Scholar
  24. Nielsen JC, Fredö CR (2006) Multi-disciplinary optimization of railway wheels. J Sound Vib 293(3–5):510–521CrossRefGoogle Scholar
  25. Okagata Y (2013) Design technologies for railway wheels and future prospects. Nippon Steel Sumitomo Metal Tech Rep 105Google Scholar
  26. Park GJ (2007) Analytic methods for design practice. Springer Science & Business MediaGoogle Scholar
  27. Park HS, Dang XP (2010) Structural optimization based on CAD–CAE integration and metamodeling techniques. Comput Aided Des 42(10):889–902CrossRefGoogle Scholar
  28. PIAnO User’s Manual (2017) PIDOTECH Inc., Seoul, KoreaGoogle Scholar
  29. Ramanan L, Kumar RK, Sriraman R (1999) Thermo-mechanical finite element analysis of a rail wheel. Int J Mech Sci 41(4–5):487–505CrossRefzbMATHGoogle Scholar
  30. Sacks J, Welch WJ, Mitchell TJ, Wynn HP (1989) Design and analysis of computer experiments. Stat Sci:409–423Google Scholar
  31. Seo JW, Goo BC, Choi JB, Kim YJ (2004) A study on the contact fatigue life evaluation for railway wheels considering residual stress variation. Trans Korean Soc Mech Eng A 28(9):1391–1398CrossRefGoogle Scholar
  32. Shevtsov IY, Markine VL, Esveld C (2005) Optimal design of wheel profile for railway vehicles. Wear 258(7–8):1022–1030CrossRefGoogle Scholar
  33. Sung KD, Yang WH, Cho MR, Chung KY (2001) A study on the shape design and stress analysis of wheel plate for rolling stock (2). Trans KSAE 3(3):221–229Google Scholar
  34. Yang L, Li B, Lv Z, Hou W, Hu P (2016) Finite element mesh deformation with the skeleton-section template. Comput Aided Des 73:11–25CrossRefGoogle Scholar
  35. Zerbst U, Mädler K, Hintze H (2005) Fracture mechanics in railway applications—an overview. Eng Fract Mech 72(2):163–194CrossRefGoogle Scholar

Copyright information

© Springer-Verlag GmbH Germany, part of Springer Nature 2019

Authors and Affiliations

  1. 1.School of Mechanical EngineeringYonsei UniversitySeoulSouth Korea
  2. 2.Fatigue and Fracture Research TeamKorea Railroad Research InstituteUiwang-siSouth Korea

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