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Buckling optimization of laminated composite plate with elliptical cutout using ANN and GA

  • Nicholas, P. Emmanuel (Department of Mechanical Engineering, PSNA College of Engineering and Technology) ;
  • Padmanaban, K.P. (Department of Mechanical Engineering, SBM College of Engineering and Technology) ;
  • Vasudevan, D. (Department of Mechanical Engineering, PSNA College of Engineering and Technology)
  • Received : 2014.01.30
  • Accepted : 2014.07.24
  • Published : 2014.11.25

Abstract

Buckling optimization of laminated composite plates is significant as they fail because of buckling under in-plane compressive loading. The plate is usually modeled without cutout so that the buckling strength is found analytically using classical laminate plate theory (CLPT). However in real world applications, the composite plates are modeled with cutouts for getting them assembled and to offer the provisions like windows, doors and control system. Finite element analysis (FEA) is used to analyze the buckling strength of the plate with cutouts and it leads to high computational cost when the plate is optimized. In this article, a genetic algorithm based optimization technique is used to optimize the composite plate with cutout. The computational time is highly reduced by replacing FEA with artificial neural network (ANN). The effectiveness of the proposed method is explored with two numerical examples.

Keywords

References

  1. Ahmet, E., Yeter, E. and Bulut, M. (2013), "The effects of cut-outs on lateral buckling behavior of laminated composite beams", Compos. Struct., 104, 54-59. https://doi.org/10.1016/j.compstruct.2013.04.019
  2. Aymerich, F. and Serra, M. (2008), "Optimization of laminate stacking sequence for maximum buckling load using the ant colony optimization (ACO) metaheuristic", Compos. Part A: Appl. Sci. Manufact., 39(2), 262-272. https://doi.org/10.1016/j.compositesa.2007.10.011
  3. Chakraborty, D. (2005), "Artificial neural network based delamination prediction in laminated composites", Mater. Des., 26(1), 1-7. https://doi.org/10.1016/j.matdes.2004.04.008
  4. Demuth, H., Beale, M. and Hagan, M. (2009), Neural Network Toolbox User's Guide, The Mathworks, Natick.
  5. Hu, H.T. and Wang, S.S. (1992), "Optimization for buckling resistance of fiber-composite laminate shells with and without cutouts", Compos. Struct., 22(1), 3-13. https://doi.org/10.1016/0263-8223(92)90034-A
  6. Iyengar, N.G.R. and Vyas, N. (2011), "Optimum design of laminated composite under axial compressive load", Sadhana, 36(1), 73-85. https://doi.org/10.1007/s12046-011-0009-5
  7. Kermanshashi, B. and Iwamiya, H. (2002), "Up to 2020 load casting using neural nets", Elec. Power Energy Syst., 24, 789-797. https://doi.org/10.1016/S0142-0615(01)00086-2
  8. Komur, A., Sen, M.F., Atas, A. and Arslan, N. (2010), "Buckling analysis of laminated composite plates with an elliptical/circular cutout using FEM", Adv. Eng. Softw., 41(2), 161-164. https://doi.org/10.1016/j.advengsoft.2009.09.005
  9. Liu, Y., Feng, J. and Qing, L. (2006), "A strength-based multiple cutout optimization in composite plates using fixed grid finite element method", Compos. Struct., 73(4), 403-412. https://doi.org/10.1016/j.compstruct.2005.02.014
  10. Lopes, C.S., Gurdal, Z. and Camanho, P.P. (2010), "Tailoring for strength of composite steered-fibre panels with cutouts", Compos. Part A: Appl. Sci. Manufact., 41(12), 1760-1767. https://doi.org/10.1016/j.compositesa.2010.08.011
  11. Mahmut, B. (2011), "Comparison of ANFIS and NN models-with a study in critical buckling load estimation", Appl. Soft Comput., 11(4), 3779-3791. https://doi.org/10.1016/j.asoc.2011.02.011
  12. Nicholas, P.E., Padmanaban, K.P. and Sofia, A.S. (2012), "Optimization of dispersed laminated composite plate for maximum safety factor using genetic algorithm and various failure criteria", Procedia Eng., 38, 1209-1217. https://doi.org/10.1016/j.proeng.2012.06.152
  13. Ozgur, E. and Sonmez, F.O. (2005), "Optimum design of composite laminates for maximum buckling load capacity using simulated annealing", Compos. Struct., 71(1), 45-52. https://doi.org/10.1016/j.compstruct.2004.09.008
  14. Qablan, A.H., Katkhuda, H. and Dwairi, H. (2009), "Assessment of buckling behavior of square composite plates with circular cutout subjected to in-plane shear", Jordan J. Civil Eng., 3(2), 184-195.
  15. Rao, A.R.M. and Arvind, N. (2005), "A scatter search algorithm for stacking sequence optimization of laminate composites", Compos. Struct., 70(4), 383-402. https://doi.org/10.1016/j.compstruct.2004.09.031
  16. Rocha, I.B.C.M., Parente, Jr. E. and Melo, A.M.C. (2014), "A hybrid shared/distributed memory parallel genetic algorithm for optimization of laminate composites", Compos. Struct., 107, 288-297. https://doi.org/10.1016/j.compstruct.2013.07.049
  17. Sebaey, T.A., Lopes, C.S., Blanco, N. and Costa, J. (2011), "Ant colony optimization for dispersed laminated composite panels under biaxial loading", Compos. Struct., 94(1), 31-36. https://doi.org/10.1016/j.compstruct.2011.07.021
  18. Sivakumar, K., Iyengar, N.G.R. and Deb, K. (1998), "Optimum design of laminated composite plates with cutouts using a genetic algorithm", Compos. Struct., 42 (3), 265-279. https://doi.org/10.1016/S0263-8223(98)00072-5
  19. Soremekun, G., Gurdal, Z., Haftka, R.T. and Watson, L.T. (2001), "Composite laminate design optimization by genetic algorithm with generalized elitist selection", Comput. Struct., 79(2), 131-143. https://doi.org/10.1016/S0045-7949(00)00125-5
  20. Tomislav, B., Ukic, S., Peternel, I., Kusic, H. and Bozic, A.L. (2014), "Artificial neural network models for advanced oxidation of organics in water matrix-Comparison of applied methodologies", Indian J. Chem. Tech., 21(1), 21-29.
  21. Topal, U. and Umit, U. (2007), "Optimum design of laminated composite plates to maximize buckling load using MFD method", Thin Wall0 Struct., 45(7), 660-669. https://doi.org/10.1016/j.tws.2007.06.002
  22. Yuen, K.V. and Lam, H.F. (2006), "On the complexity of artificial neural networks for smart structures monitoring", Eng. Struct., 28(7), 977-984. https://doi.org/10.1016/j.engstruct.2005.11.002
  23. Zheng, S.J., Li, Z.Q. and Wang, H.T. (2009), "Research on delamination monitoring for composite structures based on HHGA-WNN", Appl. Soft Comput., 9(3), 918-923. https://doi.org/10.1016/j.asoc.2008.11.008

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