Browse > Article
http://dx.doi.org/10.7842/kigas.2021.25.2.64

Prediction of Mechanical Properties and Behavior of Polymer Matrix Composites Based on Machine Learning  

Lee, Nagyeong (Dept. of Chemical Engineering, Myongji University)
Shin, Yongbeom (Dept. of Chemical Engineering, Myongji University)
Shin, Dongil (Dept. of Chemical Engineering, Myongji University)
Publication Information
Journal of the Korean Institute of Gas / v.25, no.2, 2021 , pp. 64-71 More about this Journal
Abstract
Research on polymer matrix composites with excellent molding processability and mechanical properties in the automotive field including hydrogen fuel cell electric vehicles is expanding to Computer-Aided Engineering (CAE) to support the design of materials with specific mechanical properties. CAE automation requires the prediction of the mechanical properties and behavior of materials. Unlike single materials, the mechanical properties prediction of polymer matrix composites is difficult to explain with formulas because the mechanical behavior is complicated to be explained only by the relationship between the matrix and the filler. In this study, the stress-strain curve according to the composition of polymer matrix composites, which was difficult to predict due to its sensitivity to large plastic deformation and composition, was predicted based on machine learning of the test data. The developed model finds a complex correlation between matrix and filler types and compositions, and predicts the total stress-strain curve meaningfully even in the absence of learned test data. It is expected that the material design AI system can be completed in the future based on the developed model that predicts the mechanical properties of polymer matrix composites even for the combination and composition that have not been learned.
Keywords
machine learning; mechanical behavior; polymer matrix composite; prediction model;
Citations & Related Records
연도 인용수 순위
  • Reference
1 Liu, S., Li, D., Yang, Y., and Jiang, L., "Fabrication, mechanical properties and failure mechanism of random and aligned nanofiber membrane with different parameters", Nanotechnology Reviews, 8, 218-226, (2019)   DOI
2 Abadi, M. et al., "TensorFlow: Large-Scale Machine Learning on Heterogeneous Distributed Systems", arXiv preprint, (2015)
3 Messler, R. W., The Essence of Materials for Engineers, Jones & Bartlett Learning, (2011)
4 LeCun, Y., Bengio, Y., and Hinton, G., "Deep learning", Nature, 521, (2015)
5 Sneok, J., Larochelle, H., and Adams, R. P., "Practical Bayesian Optimization of Machine Learning Algorithms", Advances in Neural Information Processing Systems, 25, 2960-2968, (2012)
6 Kingma, D. P., and Ba, J., "ADAM: A method for stochastic opimization", International Conference for Learning Representations, (2015)
7 삼정KPMG 경제연구원, "자동차 경량화 트렌드의 중심이동, 소재의 경량화", Issue monitor, 96, (2018)
8 Botchkarev, A., "Performance Metrics (Error Measures) in Machine Learning Regression, Forecasting and Prognostics: Properties and Typology", Interdisciplinary Journal of Information, 14, 45-79, (2019)