Academic Journal
A methodology to generate design allowables of composite laminates using machine learning
| العنوان: | A methodology to generate design allowables of composite laminates using machine learning |
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| المؤلفون: | Furtado, Carolina, Pereira, L.F., Tavares, Rodrigo Paiva, Salgado, Henrique M., Otero Gruer, Fermín Enrique, Catalanotti, Guiuseppe, Arteiro, Albertino, Bessa, Miguel A., Camanho, Pedro P. |
| المساهمون: | Universitat Politècnica de Catalunya. Departament de Ciència i Enginyeria Nàutiques, Universitat Politècnica de Catalunya. RMEE - Grup de Resistència de Materials i Estructures en l'Enginyeria |
| سنة النشر: | 2021 |
| المجموعة: | Universitat Politècnica de Catalunya, BarcelonaTech: UPCommons - Global access to UPC knowledge |
| مصطلحات موضوعية: | Àrees temàtiques de la UPC::Enginyeria dels materials::Materials compostos, Composite materials, Machine learning, Polymer-matrix composites (PMCs), fracture mechanics, design allowables, Materials compostos, Aprenentatge automàtic |
| الوصف: | 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. ; Postprint (author's final draft) |
| نوع الوثيقة: | article in journal/newspaper |
| وصف الملف: | 1 p.; application/pdf |
| اللغة: | English |
| Relation: | https://www.sciencedirect.com/science/article/abs/pii/S0020768321001852; Furtado, C. [et al.]. A methodology to generate design allowables of composite laminates using machine learning. "International journal of solids and structures", Desembre 2021, vol. 233, p. 111095.; http://hdl.handle.net/2117/365410 |
| DOI: | 10.1016/j.ijsolstr.2021.111095 |
| الاتاحة: | http://hdl.handle.net/2117/365410 https://doi.org/10.1016/j.ijsolstr.2021.111095 |
| Rights: | Open Access |
| رقم الانضمام: | edsbas.725A6D41 |
| قاعدة البيانات: | BASE |
| DOI: | 10.1016/j.ijsolstr.2021.111095 |
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