A methodology to generate design allowables of composite laminates using machine learning

• Published in: International journal of solids and structures Vol. 233; p. 111095
• Main Authors: Furtado, C., Pereira, L.F., Tavares, R.P., Salgado, M., Otero, F., Catalanotti, G., Arteiro, A., Bessa, M.A., Camanho, P.P.
• Format: Journal Article
• Language: English
• Published: New York Elsevier Ltd 15.12.2021; Elsevier BV
• Subjects: Algorithms; Artificial neural networks; Composite structures; Computing time; Data points; Design allowables; Failure modes; Fracture mechanics; Gaussian process; Laminates; Machine learning; Material properties; Neural networks; Polymer-matrix composites (PMCs); Representations; Ultimate tensile strength; Design allowables; Fracture mechanics; Polymer-matrix composites (PMCs); Machine learning

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 Ne...