A method for the optimal design of low-density polymer foam core sandwiches using FEA and multiobjective optimization of design variables

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Uzay C., Acer D. C., Geren N.

JOURNAL OF POLYMER ENGINEERING, cilt.42, sa.1, ss.75-84, 2022 (SCI-Expanded) identifier identifier

  • Yayın Türü: Makale / Tam Makale
  • Cilt numarası: 42 Sayı: 1
  • Basım Tarihi: 2022
  • Doi Numarası: 10.1515/polyeng-2021-0181
  • Dergi Adı: JOURNAL OF POLYMER ENGINEERING
  • Derginin Tarandığı İndeksler: Science Citation Index Expanded (SCI-EXPANDED), Scopus, Applied Science & Technology Source, Compendex, INSPEC
  • Sayfa Sayıları: ss.75-84
  • Anahtar Kelimeler: analysis of variance (ANOVA), finite element analysis (FEA), multi-objective optimization, sandwich structures, three-point bending, FAILURE MODES, PANELS, BEHAVIOR, BEAMS
  • Çukurova Üniversitesi Adresli: Evet

Özet

In this study, a generative method was introduced to determine the optimal design of low-density polymer foam core sandwiches using finite element analysis (FEA) and multi-objective optimization of design variables without needing experiments. The method was also assessed. The sandwich structures were designed based on woven plain carbon fiber fabrics, PVC foam core, and polymer epoxy matrix. The design variables are the core density (40, 48, 60 kg/m(3)) and the core thickness (16, 20, 25 mm). The sandwich configurations were subjected to FEA under the three-point bending (TPB) loads. The force-reaction curves obtained from FEA were compared to experimental data available in the literature. Excellent agreement was achieved between the experimental and FEA simulated results at the linear elastic region of the curves. Thus, it allowed predicting the bending stiffness of the sandwiches via TPB analysis. Besides, a two-way analysis of variance (ANOVA) was conducted to determine the effects of parameters on sandwich mass and bending load capacity. Multi-objective optimization of design variables was also carried out according to the constructed mathematical models. The method provided in this study eases both designer's and researcher's work to obtain the optimal design variables without making costly experiments.