Structural Engineering and Mechanics

  • 제90권4호
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  • Pages.403-415
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  • 2024
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  • 1225-4568(pISSN)
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  • 1598-6217(eISSN)
테크노프레스 (Techno-Press)
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DOI QR코드

DOI QR Code

Experimental investigating and machine learning prediction of GNP concentration on epoxy composites

  • Hatam K. Kadhom (Department of Electromechanical Engineering, University of Technology-Iraq) ;
  • Aseel J. Mohammed (Department of Electromechanical Engineering, University of Technology-Iraq)
  • 투고 : 2023.06.17
  • 심사 : 2024.04.23
  • 발행 : 2024.05.25
⟨ 이전 논문 다음 논문 ⟩

초록

We looked at how the damping qualities of epoxy composites changed when different amounts of graphite nanoplatelets (GNP) were added, from 0% to 6% by weight. A mix of free and forced vibration tests helped us find the key GNP content that makes the damper ability better the most. We also created a Representative Volume Element (RVE) model to guess how the alloys would behave mechanically and checked these models against testing data. An Artificial Neural Network (ANN) was also used to guess how these compounds would react to motion. With proper hyperparameter tweaking, the ANN model showed good correlation (R2=0.98) with actual data, indicating its ability to predict complex material behavior. Combining these methods shows how GNPs impact epoxy composite mechanical properties and how machine learning might improve material design. We show how adding GNPs to epoxy composites may considerably reduce vibration. These materials may be used in industries that value vibration damping.

키워드

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