Steel and Composite Structures

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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)
  • Received : 2018.05.25
  • Accepted : 2019.08.01
  • Published : 2019.08.25
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Abstract

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.

Keywords

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