Modeling <scp>constituent–property</scp> relationship of polyvinylchloride composites by neural networks

Reddy, Bhumi Reddy Srinivasulu; Premasudha, Mookala; Panigrahi, Bharat B.; Cho, Kwon‐Koo; Reddy, Nagireddy Gari Subba

doi:10.1002/pc.25612

Cited by 16 publications

(2 citation statements)

References 32 publications

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“…Recently, some surrogate models, such as artificial neural networks [15][16][17][18][19][20] combined with support vector machine [21] and clustering analysis [22][23][24][25] have been proposed to design process parameters and predict physical properties. These models have some advantages of short running time and accurate prediction results.…”

Section: Introductionmentioning

confidence: 99%

Determination of needling process satisfying stiffness requirements of 3D needled composites

Wang

Meng

et al. 2022

Polymer Composites

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The traditional trial-and-error method is expensive and time-consuming to determine the needling process parameters satisfying the stiffness requirements of the 3D needled composites. A new design method for these unknown needling process parameters is proposed using a convolutional neural network (CNN) surrogate model, in which a series of stress distribution images of representative volume cell (RVC) for 3D needled composites under different loads are implemented. These stress distribution images reserve both material stiffness and needling process information. The CNN surrogate model can be efficiently established to obtain the relationship between the stress distribution images and the needling process parameters. The accuracy of the training samples and the test samples in the CNN surrogate model reaches 93.85% and 87.50% respectively. An artificial RVC can be directly constructed corresponding to the stiffness properties. The unknown needling process parameters can be determined using the CNN surrogate model and the stress distribution images of the artificial RVC. The result shows the stiffness properties of 3D needled composites with the needling process parameters obtained by the CNN surrogate model are in good agreement with the experimental results, where the maximum relative error is 7.66%. This method provides a new way to design the needling process parameters satisfying the specific stiffness requirements of the 3D needled composites.

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Section: Introductionmentioning

confidence: 99%

Determination of needling process satisfying stiffness requirements of 3D needled composites

Wang

Meng

et al. 2022

Polymer Composites

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“…The use of computational tools can overcome the difficulties involved in complex nonlinear variable relationships 17–21 . An artificial neural network (ANN) is one of the computational methods used to correlate the complex and non‐linearly related variables 22,23 .…”

Section: Introductionmentioning

confidence: 99%

Correlating the 3D melt electrospun polycaprolactone fiber diameter and process parameters using neural networks

Narayana

Wang

Yeom

et al. 2021

J of Applied Polymer Sci

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In the present work, we developed an artificial neural networks (ANN) model to predict and analyze the polycaprolactone fiber diameter as a function of 3D melt electrospinning process parameters. A total of 35 datasets having various combinations of electrospinning writing process variables (collector speed, tip to nozzle distance, applied pressure, and voltage) and resultant fiber diameter were considered for model development. The designed stand‐alone ANN software extracts relationships between the process variables and fiber diameter in a 3D melt electrospinning system. The developed model could predict the fiber diameter with reasonable accuracy for both train (28) and test (7) datasets. The relative index of importance revealed the significance of process variables on the fiber diameter. Virtual melt spinning system with the mean values of the process variables identifies the quantitative relationship between the fiber diameter and process variables.

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Advances in Computational Intelligence of Polymer Composite Materials: Machine Learning Assisted Modeling, Analysis and Design

Sharma

Mukhopadhyay

Rangappa

et al. 2022

Arch Computat Methods Eng

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The superior multi-functional properties of polymer composites have made them an ideal choice for aerospace, automobile, marine, civil, and many other technologically demanding industries. The increasing demand of these composites calls for an extensive investigation of their physical, chemical and mechanical behavior under different exposure conditions. Machine learning (ML) has been recognized as a powerful predictive tool for data-driven multi-physical modeling, leading to unprecedented insights and exploration of the system properties beyond the capability of traditional computational and experimental analyses. Here we aim to abridge the findings of the large volume of relevant literature and highlight the broad spectrum potential of ML in applications like prediction, optimization, feature identification, uncertainty quantification, reliability and sensitivity analysis along with the framework of different ML algorithms concerning polymer composites. Challenges like the curse of dimensionality, overfitting, noise and mixed variable problems are discussed, including the latest advancements in ML that have the potential to be integrated in the field of polymer composites.Based on the extensive literature survey, a few recommendations on the exploitation of various ML algorithms for addressing different critical problems concerning polymer composites are provided along with insightful perspectives on the potential directions of future research.

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Modeling constituent–property relationship of polyvinylchloride composites by neural networks

Cited by 16 publications

References 32 publications

Determination of needling process satisfying stiffness requirements of 3D needled composites

Determination of needling process satisfying stiffness requirements of 3D needled composites

Correlating the 3D melt electrospun polycaprolactone fiber diameter and process parameters using neural networks

Advances in Computational Intelligence of Polymer Composite Materials: Machine Learning Assisted Modeling, Analysis and Design

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