Print Email Facebook Twitter A methodology to generate design allowables of composite laminates using machine learning Title A methodology to generate design allowables of composite laminates using machine learning Author Furtado, C. (Massachusetts Institute of Technology; INEGI) Tavares, R. P. (Universiteit Gent; INEGI) Gomes Pereira, L.P. (TU Delft Team Georgy Filonenko; INEGI; Universidade do Porto) Salgado, M. (Universidade do Porto; INEGI) Otero, F. (International Centre for Numerical Methods in Engineering (CIMNE); INEGI) Catalanotti, G. (University of Évora) Arteiro, A. (Universidade do Porto; INEGI) Bessa, M.A. (TU Delft Team Georgy Filonenko) Camanho, P. P. (Universidade do Porto; INEGI) Date 2021 Abstract This work represents the first step towards the application of machine learning techniques in the prediction of statistical design allowables of composite laminates. Building on data generated analytically, four machine algorithms (XGBoost, Random Forests, Gaussian Processes and Artificial Neural Networks) are used to predict the notched strength of composite laminates and their statistical distribution, associated to the uncertainty related to the material properties and geometrical features. This work focuses not only on the so-called Legacy Quad Laminates (0°/90°/±45°), typically used in the design of composite aerostructures, but also on the newer concept of double-double (or double-angle ply) laminates. Very good representations of the design space, translating in low generalization relative errors of around ±10%, and very accurate representations of the distributions of notched strengths around single design points and corresponding B-basis allowables are obtained. All machine learning algorithms, with the exception of the Random Forests, show very good performances, with Gaussian Processes outperforming the others for very small number of data points while Artificial Neural Networks have better performance for larger training sets. This work serves as basis for the prediction of first-ply failure, ultimate strength and failure mode of composite specimens based on non-linear finite element simulations, providing further reduction of the computational time required to virtually obtain the design allowables for composite laminates. Subject Design allowablesFracture mechanicsMachine learningPolymer-matrix composites (PMCs) To reference this document use: http://resolver.tudelft.nl/uuid:4085be97-c634-4548-ba77-2dff78fd34e1 DOI https://doi.org/10.1016/j.ijsolstr.2021.111095 ISSN 0020-7683 Source International Journal of Solids and Structures, 233 Part of collection Institutional Repository Document type journal article Rights © 2021 C. Furtado, R. P. Tavares, L.P. Gomes Pereira, M. Salgado, F. Otero, G. Catalanotti, A. Arteiro, M.A. Bessa, P. P. Camanho Files PDF 1_s2.0_S0020768321001852_main.pdf 3.85 MB Close viewer /islandora/object/uuid:4085be97-c634-4548-ba77-2dff78fd34e1/datastream/OBJ/view