전산구조공학 (Computational Structural Engineering)

한국전산구조공학회 (Computational Structural Engineering Institute of Korea)
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머신러닝 기법의 복합재 연구 활용

Machine Learning Application in Composite Research

  • 유승화 (한국과학기술원 기계공학과)
  • 발행 : 2020.06.05
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참고문헌

  1. G.X. Gu, C.-T. Chen, D.J. Richmond, and M.J. Buehler: Bioinspired hierarchical composite design using machine learning: simulation, additive manufacturing, and experiment. Mater. Horiz. 5, 939 (2018). https://doi.org/10.1039/c8mh00653a
  2. G.X. Gu, C.-T. Chen, and M.J. Buehler: De novo composite design based on machine learning algorithm. Extreme Mech. Lett. 18, 19 (2018). https://doi.org/10.1016/j.eml.2017.10.001
  3. S. Tiryaki and A. Aydirn: An artificial neural network model for predicting compression strength of heat treated woods and comparison with a multiple linear regression model. Constr. Build. Mater. 62, 102 (2014). https://doi.org/10.1016/j.conbuildmat.2014.03.041
  4. F. Khademi, M. Akbari, S.M. Jamal, and M Nikoo: Multiple linear regression, artificial neural network, and fuzzy logic prediction of 28 days compressive strength of concrete. Front. Struct. Civil Eng. 11, 90 (2017). https://doi.org/10.1007/s11709-016-0363-9
  5. B.A. Young, A, Hall, L, Pilon, P. Gupta, and G. Sant: Can the compressive strength of concrete be estimated from knowledge of the mixture proportions?: New insights from statistical analysis and machine learning methods. Cem. Concr. Res. 115, 379 (2019). https://doi.org/10.1016/j.cemconres.2018.09.006
  6. G. Tapia, A. Elwany, and H. Sang: Prediction of porosity in metal-based additive manufacturing using spatial Gaussian process models. Addit. Manuf. 12, 282 (2016).
  7. Heeyeong Jeong, Stefano Signetti, Tong-seok Han, and Seunghwa Ryu* "Phase field modeling of crack propagation under combined shear and tensile loading with hybrid formulation", Computational Materials Science 155, 438 (2018).
  8. Charles Yang+, Youngsoo Kim+, Seunghwa Ryu*, and Grace Gu*, "Using Convolutional Neural Networks to Predict Composite Properties beyond the Elastic Limit", MRS Communications 9, 609 (2019). https://doi.org/10.1557/mrc.2019.49
  9. Charles Yang+, Youngsoo Kim+, Seunghwa Ryu*, and Grace Gu*, "Prediction of composite microstructure stress-strain curves using convolutional neural networks", Materials & Design 189, 108509 (2020). https://doi.org/10.1016/j.matdes.2020.108509
  10. Yongtae Kim, Charles Yang, Youngsoo Kim, Grace Gu, and Seunghwa Ryu*, "Designing Adhesive Pillar Shape with Deep Learning-Based Optimization", ACS Applied Materials & Interface accepted.