Neural networks modeling of strain energy density and Tsai-Wu index in laminated composites

Elías Ledesma-Orozco; Julio C. Galvis-Chacón; Rodríguez-Sánchez, Alejandro E.

doi:10.1177/00219983241235856

Neural networks modeling of strain energy density and Tsai-Wu index in laminated composites

Journal

Journal of Composite Materials

ISSN

0021-9983

1530-793X

Date Issued

2024

Author(s)

Elías Ledesma-Orozco

Julio C. Galvis-Chacón

Rodríguez-Sánchez, Alejandro E.

Facultad de Ingeniería - CampGDL

Type

text::journal::journal article

DOI

10.1177/00219983241235856

URL

https://scripta.up.edu.mx/handle/20.500.12552/10153

Abstract

In laminated composite materials design, optimization mainly targets the stacking sequence configuration, which is defined by the lamina thickness and fiber orientations within each layer. Recent studies emphasize the increasing role of Machine Learning in promoting innovative composite designs by facilitating the accurate modeling of essential properties such as strength and stiffness. This study introduces two metamodels that utilize feed-forward artificial neural networks, taking laminate thickness and fiber steering angles as input parameters. The output variables, including strain energy density and the Tsai-Wu failure index, enable the prediction of stacking sequence configurations for laminated materials, a capability confirmed in a case study. The results showcase neural network models with the ability to predict these variables, achieving coefficients of determination above 0.90 for testing data. Consequently, this modeling approach has the potential to be a tool for designers, aiding in decision-making processes for the subsequent optimization of stiffness and strength in structural components made of laminated composite materials.