Comparison of Models to Predict Mechanical Properties of FR-AM Composites and a Fractographical Study

León-Becerra, Juan; González‐Estrada, Octavio Andrés; Acevedo, Heller Sánchez

doi:10.3390/polym14173546

Cited by 13 publications

(7 citation statements)

References 59 publications

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“…made generalization difficult. Similar conclusions were drawn for the prediction of the mechanical properties of fiber-reinforced polymer composites using different ML algorithms [ 67 ]. Nonetheless, the differences in performance between the ANN and SVM models were relatively small, and both were successfully applied to predict the Young’s modulus of the hybrid nanocomposites developed herein.…”

Section: Resultssupporting

confidence: 74%

Synthesis and Characterization of Polyhydroxyalkanoate/Graphene Oxide/Nanoclay Bionanocomposites: Experimental Results and Theoretical Predictions via Machine Learning Models

Champa-Bujaico,

Díez-Pascual,

García-Díaz

2023

Biomolecules

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Predicting the mechanical properties of multiscale nanocomposites requires simulations that are costly from a practical viewpoint and time consuming. The use of algorithms for property prediction can reduce the extensive experimental work, saving time and costs. To assess this, ternary poly(hydroxybutyrate-co-hydroxyvalerate) (PHBV)-based bionanocomposites reinforced with graphene oxide (GO) and montmorillonite nanoclay were prepared herein via an environmentally friendly electrochemical process followed by solution casting. The aim was to evaluate the effectiveness of different Machine Learning (ML) models, namely Artificial Neural Network (ANN), Decision Tree (DT), and Support Vector Machine (SVM), in predicting their mechanical properties. The algorithms’ input data were the Young’s modulus, tensile strength, and elongation at break for various concentrations of the nanofillers (GO and nanoclay). The correlation coefficient (R2), mean absolute error (MAE), and mean square error (MSE) were used as statistical indicators to assess the performance of the models. The results demonstrated that ANN and SVM are useful for estimating the Young’s modulus and elongation at break, with MSE values in the range of 0.64–1.0% and 0.14–0.28%, respectively. On the other hand, DT was more suitable for predicting the tensile strength, with the indicated error in the range of 0.02–9.11%. This study paves the way for the application of ML models as confident tools for predicting the mechanical properties of polymeric nanocomposites reinforced with different types of nanofiller, with a view to using them in practical applications such as biomedicine.

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Section: Resultssupporting

confidence: 74%

Synthesis and Characterization of Polyhydroxyalkanoate/Graphene Oxide/Nanoclay Bionanocomposites: Experimental Results and Theoretical Predictions via Machine Learning Models

Champa-Bujaico,

Díez-Pascual,

García-Díaz

2023

Biomolecules

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“…In structural models, the mechanical interaction of the microstructures of the filler and the matrix is simulated. This approach is called structural or micromechanical and has been actively developing recently [ 48 , 49 , 50 , 51 ].…”

Section: Methodsmentioning

confidence: 99%

Computer Simulation of Composite Materials Behavior under Pressing

et al. 2022

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Composite materials have a wide range of functional properties, which is ensured by using various technological methods of obtaining both the matrix or fillers and the composition as a whole. A special place belongs to the composition formation technology, which ensures the necessary structure and properties of the composite. In this work, a computer simulation was carried out to identify the main dependencies of the behavior of composite materials in the process of the main technological operations of their production: pressing and subsequent sintering. A polymer matrix randomly reinforced with two types of fillers: spherical and short cylindrical inclusions, was used to construct the finite element models of the structure of composites. The ANSYS Workbench package was used as a calculation simulation platform. The true stress–strain curves for tension, Poisson’s ratios, and ultimate stresses for composite materials were obtained using the finite element method based on the micromechanical approach at the first stage. These values were calculated based on the stretching diagrams of the matrix and fillers and the condition of the ideality of their joint operation. At the second stage, the processes of mechanical pressing of composite materials were modelled based on their elastic–plastic characteristics from the first stage. The result is an assessment of the accumulation of residual strains at the stage before sintering. The degree of increase in total strain capability of composite materials after sintering was shown.

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“…The material properties used for the analysis correspond to the combination of glass fiber and nylon (Tables 1 and 2) present in 3D printing. [23][24][25][26] Based on previous works, 27 the selected material stack configuration for the discrete model is symmetric, indicating that the orientation of fiber layers above the midplane mirrors those below. That aims to minimize the coupling effect described by matrix B in equations ( 4) and ( 5).…”

Section: Laminated Composite Materialsmentioning

confidence: 99%

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

Ledesma-Orozco,

Galvis-Chacón,

Rodríguez-Sánchez

2024

Journal of Composite Materials

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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.

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Comparison of Models to Predict Mechanical Properties of FR-AM Composites and a Fractographical Study

Cited by 13 publications

References 59 publications

Synthesis and Characterization of Polyhydroxyalkanoate/Graphene Oxide/Nanoclay Bionanocomposites: Experimental Results and Theoretical Predictions via Machine Learning Models

Synthesis and Characterization of Polyhydroxyalkanoate/Graphene Oxide/Nanoclay Bionanocomposites: Experimental Results and Theoretical Predictions via Machine Learning Models

Computer Simulation of Composite Materials Behavior under Pressing

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

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