Mixed modes crack propagation of orthogonal woven-layer in carbon/aramid/epoxy laminates

Fu, Yuan; Lv, Wei Yan; Sun, Wen Hai; Xu, Li Min; Guo, Hui

doi:10.1016/j.compositesa.2023.107929

Composites Part A: Applied Science and Manufacturing

2024

DOI: 10.1016/j.compositesa.2023.107929

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Mixed modes crack propagation of orthogonal woven-layer in carbon/aramid/epoxy laminates

Yuan Fu,

Wei Yan Lv,

Wen Hai Sun

et al.

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2024

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Cited by 3 publications

References 47 publications

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Integrating experimental and numerical analyses for microscale tensile behavior of ceramic particle reinforced TRIP steel composites: A study on local deformation and damage evolution

Chiu,

Prabhakar,

Tseng

et al. 2024

Composites Part A: Applied Science and Manufacturing

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Integrating experimental and numerical analyses for microscale tensile behavior of ceramic particle reinforced TRIP steel composites: A study on local deformation and damage evolution

Chiu,

Prabhakar,

Tseng

et al. 2024

Composites Part A: Applied Science and Manufacturing

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Energy transfer and release during the fracture of UD fiber-reinforced thermoplastic composites

Wu,

Fu,

et al. 2024

Theoretical and Applied Fracture Mechanics

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Boosting machine learning algorithms for predicting the macroscopic material behavior of continuous fiber reinforced composite

Tariq,

Polat,

Deliktaş

2024

Journal of Reinforced Plastics and Composites

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Macroscopic mechanical properties of fibrous materials are often characterized by modeling their microscale behavior using micromechanical techniques. This process typically involves using a Representative Volume Element (RVE) and finite element simulations to obtain the macroscopic behavior through homogenization. However, these micromechanical simulations can be computationally demanding, especially for 3D models with discrete material microstructures. This paper uses boosting machine learning algorithms to predict the homogenized macroscopic material behavior of heterogeneous composites. These models are trained on the micromechanical simulation results generated by varying the constitutive parameters of local phases and microscopic parameters such as fiber volume fraction. The Bayesian optimization is used to determine the best hyperparameters of the considered boosting models, which include adaptive boosting (AdaB), gradient boosting (GBR), light gradient boosting (LGB), and extreme gradient boosting (XGB). The performances of trained models are assessed using various metrics such as R2, MAE, MAPE, and RMSE and using various plots such as scatter plots, Taylor plots, radar plots, and bar plots. The comparative assessment showed that all the models predicted the homogenized stiffness matrix of the RVE successfully, with R2 values between 0.94 and 0.99. The XGB model presented the best overall performance. This work contributes to the field of composites by presenting a new and computationally efficient approach to predict the macroscopic behavior of RVEs using boosting models.

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Mixed modes crack propagation of orthogonal woven-layer in carbon/aramid/epoxy laminates

Cited by 3 publications

References 47 publications

Integrating experimental and numerical analyses for microscale tensile behavior of ceramic particle reinforced TRIP steel composites: A study on local deformation and damage evolution

Integrating experimental and numerical analyses for microscale tensile behavior of ceramic particle reinforced TRIP steel composites: A study on local deformation and damage evolution

Energy transfer and release during the fracture of UD fiber-reinforced thermoplastic composites

Boosting machine learning algorithms for predicting the macroscopic material behavior of continuous fiber reinforced composite

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