Original Article
  • Prediction of Elastic Modulus of Woven CFRP Based on Weaving Patterns Using Deep Neural Networks
  • S. Kwon*, Cheol Min Shin**, Hyun Woo Kim**, Sang Deok Kim**, Chung Woo Park**, Seong S. Cheon*†

  • * Department of Mechanical Engineering, Graduated School, Kongju National University
    ** Kwangsung corporation LTD

  • 심층 신경망을 활용한 직물 CFRP의 직조 패턴 기반 탄성계수 예측
  • 권승호* · 신철민** · 김현우** · 김상덕** · 박충우** · 전성식*†

  • 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. Yang, H.S., Jung, W.C., Shin, K.B., and Kong, M.S., “High Temperature Tensile Stress Behavior of Hydrogen Vessel Composite Materials for Hydrogen Fuel Cell Bus,” Journal of Composite Research, Vol. 35, No. 6, 2022, pp. 425-430.
  •  
  • 2. Ji, S.M., Ham, S.W., and Cheon, S.S., “Stiffness Enhancement of Piecewise Integrated Composite Robot Arm using Machine Learning,” Journal of Composite Research, Vol. 35, No. 5, 2022, pp. 303-308.
  •  
  • 3. Reddy, S.S.P., Suresh, R., MB, H., and Shivakumar, B.P, “Use of composite materials and hybrid composites in wind turbine blades,” Journal of Materialstoday : Proceedings, Vol. 46, Part. 7, 2021, pp. 2827-2830.
  •  
  • 4. Bandaru, A.K., Sachan, Y., Ahmad, S., Alagirusamy, R., and Bhatnagar, N., “On the mechanical response of 2D plain woven and 3D angle-interlock fabrics,” Journal of Composite Part B, Vol. 118, 2017, pp. 135-148.
  •  
  • 5. Donadon, M.V., Falzon, B.G., Iannucci, L., and Hodgkinson, J.M., “A 3-D micromechanical model for predicting the elastic behaviour of woven laminates,” Journal of Composites Science and Technology, Vol. 67, No. 11–12, 2007, pp. 2467–2477.
  •  
  • 6. Hwang, Y.T., Lim, J.Y., Nam, B.G., and Kim, H.S., “Analytical Prediction and Validation of Elastic Behavior of Carbon-Fiber-Reinforced Woven Composites,” Journal of Composite Research, Vol. 31, No. 5, 2018, pp. 276-281.
  •  
  • 7. Li, H., Bacarreza, O., Khodaei, Z.S., and Aliabadi, M.H.F., “Numerical modelling of 2D woven composites by the Projective Element Method,” International Journal of Solids and Structures, 111946, 2022, pp. 254-255.
  •  
  • 8. Jin, H., An, N., Jia, Q., Ma, X., and Zhou, J., “A mesoscale computational approach to predict ABD matrix of thin woven composites,” Journal of Composite Structures, Vol. 337, 2024, 118031.
  •  
  • 9. Choi, K.H., Hwang, Y.T., Kim, H.J., and Kim, H.S., “Progressive failure analysis of woven composites considering structural characteristics based on micro-mechanics,” Journal of Compostie Strictures, Vol. 224, No. 15, 2019, 110990.
  •  
  • 10. Kim, D.J., Kim, G.W., Baek, J.H., Nam, B.G., and Kim, H.S., “Prediction of stress-strain behavior of carbon fabric woven composites by deep neural network,” Journal of Composite Structures, Vol. 318, No. 15, 2023, 117073.
  •  
  • 11. Ghane, E., Fagerström, M., and Mirkhalaf, S. M., “A multiscale deep learning model for elastic properties of woven composites,” International Journal of Solids and Structures, Vol. 282, No. 15, 2023, 112452.
  •  
  • 12. Brown, L.P., and Long, A.C., “8-Modeling the geometry of textile reinforcements for composites: TexGen,” Composite Reinforcements for Optimum Performance, 2021, pp. 237-265.
  •  
  • 13. Cao, Y., Cai, Y., Zhao, Z., Liu, P., Han, L., and Zhang, C., “Predicting the tensile and compressive failure behavior of angle-ply spread tow woven composites,” Journal of Composite Structures, Vol. 318, No. 15, 2020, 117073.
  •  
  • 14. Ji, S.M., Cho, C., W., and Cheon, S.S., “Stochastic strength Analysis according to initial void defects in composite materials”, Journal of Composite Research, Vol. 37, No. 3, 2024, pp. 179-185.
  •  
  • 15. Xia, Z., Zhang, Y., and Ellyin, F., “A unified periodical boundary conditions for representative volume elements of composites and applications,” International Journal of Solids and Structures, Vol. 40, Issue 8, 2003, pp. 1907-1921.
  •  
  • 16. Schmidhuber, J., “Deep Learning in neural networks: An overview. In Neural Networks,” Neural Networks Vol. 61, 2015, pp. 85-117.
  •  
  • 17. Zhou zhihua, Machine Learning, J Pub. Co., Ltd., China, 2020, pp. 37-41.
  •  

This Article

Correspondence to

  • Seong S. Cheon
  • Department of Mechanical Engineering, Graduated School, Kongju National University

  • E-mail: sscheon@kongju.ac.kr