Steel and Composite Structures

  • 제32권4호
  • /
  • Pages.455-466
  • /
  • 2019
  • /
  • 1229-9367(pISSN)
  • /
  • 1598-6233(eISSN)
테크노프레스 (Techno-Press)
권호 표지
DOI QR코드

DOI QR Code

A response surface modelling approach for multi-objective optimization of composite plates

  • Kalita, Kanak (Department of Mechanical Engineering, Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology) ;
  • Dey, Partha (Department of Mechanical Engineering, Academy of Technology) ;
  • Joshi, Milan (Department of Applied Science & Humanities, SVKM's NMIMS Mukesh Patel School of Technology Management & Engineering) ;
  • Haldar, Salil (Department of Aerospace Engineering and Applied Mechanics, Indian Institute of Engineering, Science and Technology)
  • 투고 : 2018.05.25
  • 심사 : 2019.08.01
  • 발행 : 2019.08.25
⟨ 이전 논문 다음 논문 ⟩

초록

Despite the rapid advancement in computing resources, many real-life design and optimization problems in structural engineering involve huge computation costs. To counter such challenges, approximate models are often used as surrogates for the highly accurate but time intensive finite element models. In this paper, surrogates for first-order shear deformation based finite element models are built using a polynomial regression approach. Using statistical techniques like Box-Cox transformation and ANOVA, the effectiveness of the surrogates is enhanced. The accuracy of the surrogate models is evaluated using statistical metrics like $R^2$, $R^2{_{adj}}$, $R^2{_{pred}}$ and $Q^2{_{F3}}$. By combining these surrogates with nature-inspired multi-criteria decision-making algorithms, namely multi-objective genetic algorithm (MOGA) and multi-objective particle swarm optimization (MOPSO), the optimal combination of various design variables to simultaneously maximize fundamental frequency and frequency separation is predicted. It is seen that the proposed approach is simple, effective and good at inexpensively producing a host of optimal solutions.

키워드

참고문헌

  1. Abachizadeh, M. and Tahani, M. (2009), "An ant colony optimization approach to multi-objective optimal design of symmetric hybrid laminates for maximum fundamental frequency and minimum cost", Struct. Multidiscipl. Optimiz., 37(4), 367-376. https://doi.org/10.1007/s00158-008-0235-6
  2. Abu-Odeh, A.Y. and Jones, H.L. (1998), "Optimum design of composite plates using response surface method", Compos. Struct., 43(3), 233-242. https://doi.org/10.1016/S0263-8223(98)00109-3
  3. Boussaid, I., Lepagnot, J. and Siarry, P. (2013), "A survey on optimization metaheuristics", Info. Sci., 237, 82-117. https://doi.org/10.1016/j.ins.2013.02.041
  4. Box, G.E. (1964), "An analysis of transformations", J. Royal Statist. Soc. Series B (Methodological), 26, 211-252. https://doi.org/10.1111/j.2517-6161.1964.tb00553.x
  5. Box, G.E. and Wilson, K.B. (1992), "On the experimental attainment of optimum conditions", In: Breakthroughs in Statistics, Springer, pp. 270-310.
  6. Consonni, V., Ballabio, D. and Todeschini, R. (2010), "Evaluation of model predictive ability by external validation techniques", J. Chemomet., 24, 194-201. https://doi.org/10.1002/cem.1290
  7. De Jong, K. (2007), Parameter setting in EAs: a 30 year perspective, Berlin, Heidelberg: Springer.
  8. Dey, S., Mukhopadhyay, T., Khodaparast, H.H. and Adhikari, S. (2016), "A response surface modelling approach for resonance driven reliability based optimization of composite shells", Periodica Polytechnica. Civil Eng., 60(1), 103. https://doi.org/10.3311/PPci.8073
  9. Eberhart, R. and Kennedy, J. (1995), "A new optimizer using particle swarm theory", Proceedings of the Sixth International Symposium on Micro Machine and Human Science, MHS'95, pp. 39-43. https://doi.org/10.1109/MHS.1995.494215
  10. Fang, Y. and Tee, K.F. (2017), "Structural reliability analysis using response surface method with improved genetic algorithm", Struct. Eng. Mech., Int. J., 62(2), 139-142. https://doi.org/10.12989/sem.2017.62.2.139
  11. Ganguli, R. (2002), "Optimum design of a helicopter rotor for low vibration using aeroelastic analysis and response surface methods", J. Sound Vib., 258(2), 327-344. https://doi.org/10.1006/jsvi.2002.5179
  12. Goldberg, D.E. (2006), Genetic Algorithms, New Delhi: Pearson Education India.
  13. Hardy, R.L. (1971), "Multiquadric equations of topography and other irregular surfaces", J. Geophys. Res., 76(8), 1905-1915. https://doi.org/10.1029/JB076i008p01905
  14. Haykin, S.S. (2001), Neural networks: a comprehensive foundation, Prentice Hall PTR.
  15. Heinonen, O. and Pajunen, S. (2011), "Optimal design of stiffened plate using metamodeling techniques", J. Struct. Mech., 44(3), 218-230.
  16. Jakob, W. and Blume, C. (2014), "Pareto optimization or cascaded weighted sum: A comparison of concepts", Algorithms, 7(1), 166-185. https://doi.org/10.3390/a7010166
  17. Ju, S., Shenoi, R.A., Jiang, D. and Sobey, A.J. (2013), "Multi-parameter optimization of lightweight composite triangular truss structure based on response surface methodology", Compos. Struct., 97, 107-116. https://doi.org/10.1016/j.compstruct.2012.10.025
  18. Kalita, K. and Haldar, S. (2017), "Eigenfrequencies of Simply Supported Taper Plates with cut-outs", Struct. Eng. Mech., Int. J., 63(1), 103-113. https://doi.org/10.12989/sem.2017.63.1.103
  19. Kalita, K., Ramachandran, M., Raichurkar, P., Mokal, S.D. and Haldar, S. (2016), "Free vibration analysis of laminated composites by a nine node isoparametric plate bending element", Adv. Compos. Lett., 25(5), 108. https://doi.org/10.1177/096369351602500501
  20. Kalita, K., Shivakoti, I. and Ghadai, R.K. (2017), "Optimizing process parameters for laser beam micro-marking using genetic algorithm and particle swarm optimization", Mater. Manuf. Process., 32(10), 1101-1108. https://doi.org/10.1080/10426914.2017.1303156
  21. Kalita, K., Dey, P. and Haldar, S. (2019a), "Robust genetically optimized skew laminates", Proceedings of the Institution of Mechanical Engineers, Part C: J. Mech. Eng. Sci., 233(1), 146-159. https://doi.org/10.1177/0954406218756943
  22. Kalita, K., Dey, P. and Haldar, S. (2019b), "Search for accurate RSM metamodels for structural engineering", J. Reinforced Plast. Compos. https://doi.org/10.1177/0731684419862346
  23. Kim, D., Kim, D.H., Cui, J., Seo, H.Y. and Lee, Y.H. (2009), "Iterative neural network strategy for static model identification of an FRP deck", Steel Compos. Struct., Int. J., 9(5), 445-455. https://doi.org/10.12989/scs.2009.9.5.445
  24. Mills, K.L., Filliben, J.J. and Haines, A.L. (2015), "Determining relative importance and effective settings for genetic algorithm control parameters", Evolut. Computat., 23(2), 309-342. https://doi.org/10.1162/EVCO_a_00137
  25. Mukhopadhyay, T., Dey, T.K., Chowdhury, R. and Chakrabarti, A. (2015), "Structural damage identification using response surface based multi-objective optimization: a comparative study", Arab. J. Sci. Eng., 40, 1027-1044. https://doi.org/10.1007/s13369-015-1591-3
  26. Pajunen, S. and Heinonen, O. (2014), "Automatic design of marine structures by using successive response surface method", Struct. Multidiscipl. Optimiz., 49(5), 863-871. https://doi.org/10.1007/s00158-013-1013-7
  27. Pan, S.-S., Lei, S., Tan, Y.-G. and Zhang, Z. (2011), "Reliability analysis for lateral stability of tongwamen bridge", Steel Compos. Struct., Int. J., 11(5), 423-434. https://doi.org/10.12989/scs.2011.11.5.423
  28. Ragavendran, U., Ghadai, R.K., Bhoi, A.K., Ramachandran, M. and Kalita, K. (2018), "Sensitivity analysis and optimization of EDM process", Transact. Can. Soc. Mech. Eng., 43(1), 13-25. https://doi.org/10.1139/tcsme-2018-0021
  29. Shooshtari, A. and Razavi, S. (2010), "A closed form solution for linear and nonlinear free vibrations of composite and fiber metal laminated rectangular plates", Compos. Struct., 92(11), 2663-2675. https://doi.org/10.1016/j.compstruct.2010.04.001

피인용 문헌

  1. Topology and size optimization of truss structures using an improved crow search algorithm vol.77, pp.6, 2019, https://doi.org/10.12989/sem.2021.77.6.779
  2. Influence of the Height in a Colombian Multi-Tunnel Greenhouse on Natural Ventilation and Thermal Behavior: Modeling Approach vol.13, pp.24, 2019, https://doi.org/10.3390/su132413631