Original Article
  • 딥러닝 기법을 적용한 평직 CFRP 복합재의 유효물성 예측

  • Yuseon Lee*, **, Dong-Hyeop Kim***, Sang-Woo Kim*, **, ****†

  • * Department of Aerospace and Mechanical Engineering, Korea Aerospace University
    ** Department of Smart Air Mobility, Korea Aerospace University
    *** LIG Defense&Aerospace
    **** Department of Aeronautical and Astronautical Engineering, Korea Aerospace University

  • Deep Learning–Based Prediction of Effective Properties of CFRP Plain-Woven Composites

  • 이유선*, ** · 김동협*** · 김상우*, **, ****†

  • This article is an open access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (http://creativecommons.org/licenses/by-nc/4.0) which permits unrestricted non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited.

References
  • 1. Kim, D.H., Kim, S.W., Kim, H.K., Kim, S.C., and Shin, B.G., “Study on Verification Methodology of Airworthiness Requirements for Bird Strike on Civilian Helicopter based on Numerical Analysis,” Journal of the Korean Society for Aeronautical & Space Sciences, Vol. 13, No. 6, 2019, pp. 70-79.
  •  
  • 2. Baek, S.M., Lim, S.J., Kim, M.S., Ko, M.G., and Park, C.I., “Study on Mechanical Properties of CFRP Composite Orthogonal Grid Structure,” Composites Research, Vol. 31, No. 2, 2018, pp. 69-75.
  •  
  • 3. Lee, Y., Kim, D.H. and Kim, S.W., “Prediction of Curing Deformation in CFRP Composite Structure Using Convolutional Neural Network Model”, Composites Research, Vol. 38, No. 2, 2025, pp. 106-113.
  •  
  • 4. Sharma, A., Mukhopadhyay, T., Rangappa, S. M., Siengchin, S., and Kushvaha, V., “Advances in Computational Intelligence of Polymer Composite Materials: Machine Learning Assisted Modeling, Analysis and Design,” Archives of Computational Methods in Engineering, Vol. 29, No. 5, 2022, pp. 3341-3385.
  •  
  • 5. Liu, X., Tian, S., Tao, F., and Yu, W., “A Review of Artificial Neural Networks in the Constitutive Modeling of Composite Materials,” Composites Part B: Engineering, Vol. 224, 2021, 109152.
  •  
  • 6. Seyhan, A.T., Tayfur, G., Karakurt, M., and Tanogˇlu, M., “Artificial Neural Network (ANN) Prediction of Compressive Strength of VARTM Processed Polymer Composites,” Computational Materials Science, Vol. 34 No. 1, 2005, pp. 99-105.
  •  
  • 7. Ramasamy, P., and Sampathkumar, S., “Prediction of Impact Damage Tolerance of Drop Impacted WGFRP Composite by Artificial Neural Network Using Acoustic Emission Parameters,” Composites Part B: Engineering, Vol. 60, 2014, pp. 457-462.
  •  
  • 8. Mini, M.K., and Sowmya, M., “Neural Network Paradigms for Fatigue Strength Prediction of Fiber-reinforced Composite Materials,” International Journal of Advanced Structural Engineering, Vol. 4, No.1, 2012, pp. 7.
  •  
  • 9. Vassilopoulos, A.P., Georgopoulos, E.F., and Dionysopoulos, V., “Artificial Neural Networks in Spectrum Fatigue Life Prediction of Composite Materials,” International Journal of Fatigue, Vol. 29, No. 1, 2007, pp. 20-29.
  •  
  • 10. Brown, L.P., and Long, A.C., “Modeling the geometry of textile reinforcements for composites: TexGen,” Composite Reinforcements for Optimum Performance, 2021, pp. 237-265.
  •  
  • 11. Omairey, S.L., Dunning, P.D., and Sriramula, S., “Development of an ABAQUS Plugin Tool for Periodic RVE Homogenisation,” Engineering with Computers, Vol. 35, No. 2, 2019, pp. 567-577.
  •  
  • 12. Liu, X., Guan, Z., Wang, X., Jiang, T., Geng, K., and Li, Z., “Study on Cure-induced Residual Stresses and Spring-in Deformation of L-shaped Composite Laminates Using a Simplified Constitutive Model Considering Stress Relaxation,” Composite Structures, Vol. 272, 2021, pp. 114203.
  •  

This Article

Correspondence to

  • Sang-Woo Kim

  • * Department of Aerospace and Mechanical Engineering, Korea Aerospace University
    ** Department of Smart Air Mobility, Korea Aerospace University
    **** Department of Aeronautical and Astronautical Engineering, Korea Aerospace University

  • E-mail: swkim@kau.ac.kr