Machine Learning Algorithms to Estimate Composite Mechanical Properties

• Janaina Gomide Federal University of Rio de Janeiro
• Lucas Vignoli Federal University of Rio de Janeiro
• Yuri Macedo Federal University of Rio de Janeiro

DOI: https://doi.org/10.5753/eniac.2023.234255

Abstract

Structures made of composite materials have been implemented in several sectors such as transport, civil construction, maritime and aerospace. The focus of this study is unidirectional composites that have 5 independent properties. Obtaining these properties can be done experimentally, numerically and analytically. In this article we propose an alternative way to estimate these properties, using machine learning algorithms. The goal of this paper is to evaluate machine learning algorithms to generate the estimation of these 5 properties of composites. Experiments were carried out with two distinct data sets and the results obtained were satisfactory.

References

Benzarti, K., Cangemi, L., and Dal Maso, F. (2001). Transverse properties of unidirectional glass/epoxy composites: influence of fibre surface treatments. Composites Part A: Applied Science and Manufacturing, 32(2):197–206.

Bledzki, A., Kessler, A., Rikards, R., and Chate, A. (1999). Determination of elastic constants of glass/epoxy unidirectional laminates by the vibration testing of plates. Composites Science and Technology, 59(13):2015–2024.

Bravo-Castillero, J., Guinovart-Díaz, R., Rodríguez-Ramos, R., Sabina, F. J., and Brenner, R. (2012). Unified analytical formulae for the effective properties of periodic fibrous composites. Materials Letters, 73:68–71.

Camanho, P., Maimí, P., and Dávila, C. (2007). Prediction of size effects in notched laminates using continuum damage mechanics. Composites Science and Technology, 67(13):2715–2727.

Géron, A. (2019). Hands-on machine learning with Scikit-Learn, Keras, and TensorFlow: Concepts, tools, and techniques to build intelligent systems. O’Reilly Media.

Guo, P., Meng, W., Xu, M., Li, V. C., and Bao, Y. (2021). Predicting mechanical properties of high-performance fiber-reinforced cementitious composites by integrating micromechanics and machine learning. Materials, 14(12):3143.

Huang, H. and Talreja, R. (2005). Effects of void geometry on elastic properties of unidirectional fiber reinforced composites. Composites Science and Technology, 65(13):1964–1981.

Huang, Z.-m. (2001). Micromechanical prediction of ultimate strength of transversely isotropic fibrous composites. International journal of solids and structures, 38(22-23):4147–4172.

Kaddour, A. and Hinton, M. (2012). Input data for test cases used in benchmarking triaxial failure theories of composites. Journal of Composite Materials, 46(19-20):2295–2312.

Kaddour, A., Hinton, M., Smith, P., and Li, S. (2013). The background to the third world-wide failure exercise. Journal of Composite Materials, 47(20-21):2417–2426.

Kriz, R. and Stinchcomb, W. (1979). Elastic moduli of transversely isotropic graphite fibers and their composites. Experimental Mechanics, 19(2):41–49.

Lee, J. and Soutis, C. (2007). A study on the compressive strength of thick carbon fibre–epoxy laminates. Composites Science and Technology, 67(10):2015–2026.

Li, W., Cai, H., and Zheng, J. (2014). Characterization of strength of carbon fiber reinforced polymer composite based on micromechanics. Polymers and Polymer Composites, 22(2):105–116.

Merayo, D., Rodríguez-Prieto, A., and Camacho, A. M. (2020). Prediction of physical and mechanical properties for metallic materials selection using big data and artificial neural networks. IEEE Access, 8:13444–13456.

Pathan, M., Ponnusami, S., Pathan, J., Pitisongsawat, R., Erice, B., Petrinic, N., and Tagarielli, V. (2019). Predictions of the mechanical properties of unidirectional fibre composites by supervised machine learning. Scientific reports, 9(1):1–10.

Rajput, R., Raut, A., and Setti, S. G. (2022). Prediction of mechanical properties of aluminium metal matrix hybrid composites synthesized using stir casting process by machine learning. Materials Today: Proceedings, 59:1735–1742.

Schaefer, J., Werner, B., and Daniel, I. M. (2014). Strain-rate-dependent failure of a toughened matrix composite. Experimental Mechanics, 54(6):1111–1120.

Shahinur, S. and Hasan, M. (2020). Natural fiber and synthetic fiber composites: Comparison of properties, performance, cost and environmental benefits.

Soden, P., Hinton, M., and Kaddour, A. (1998). Lamina properties, lay-up configurations and loading conditions for a range of fibre-reinforced composite laminates. Composites Science and Technology, 58(7):1011–1022.

Tsai, S. and Hahn, H. (1980). Introduction to composite materials, technomic publ. Co., Westport.

Ventura, A. M. F. (2009). Os compósitos e a sua aplicação na reabilitação de estruturas metálicas. Ciência & Tecnologia dos Materiais, 21(3-4):10–19.

Vignoli, L. L., Savi, M. A., Pacheco, P. M., and Kalamkarov, A. L. (2019). Comparative analysis of micromechanical models for the elastic composite laminae. Composites Part B: Engineering, 174:106961.

Wang, W., Wang, H., Zhou, J., Fan, H., and Liu, X. (2021). Machine learning prediction of mechanical properties of braided-textile reinforced tubular structures. Materials & Design, 212:110181.

Yim, J. H. and Gillespie Jr, J. (2000). Damping characteristics of 0° and 90° as4/3501-6 unidirectional laminates including the transverse shear effect. Composite Structures, 50(3):217–225.