Kriging‐based orthotropic closure for flow‐induced fiber orientation and the part stiffness predictions with experimental investigation

Su, Dong‐Xiao; Zhao, Jian; Wang, Yan; Qu, Minjie

doi:10.1002/pc.25243

Cited by 5 publications

(4 citation statements)

References 23 publications

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“…Kriging has been successfully used to classify and cluster data with accuracy [302]. Considering the simplicity and reduced computational time of Gaussian process and kriging, in recent years this methodology has been used extensively to model the physical properties of different polymer composites with fairly good accuracy [303][304][305][306][307][308][309][310][311][312][313][314][315][316][317].…”

Section: Gaussian Process (Gp) and Krigingmentioning

confidence: 99%

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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Section: Gaussian Process (Gp) and Krigingmentioning

confidence: 99%

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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show abstract

“…Under the condition of small samples, a certain fitting accuracy can be ensured by the Kriging model, which has been well applied to the part stiffness predictions [20], and the reducing of part residual stress and warpage [21].…”

Section: Kriging Modelmentioning

confidence: 99%

Study of Residual Wall Thickness and Multiobjective Optimization for Process Parameters of Water-Assisted Injection Molding

Yang

2020

Advances in Polymer Technology

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Residual wall thickness is an important indicator for water-assisted injection molding (WAIM) parts, especially the maximization of hollowed core ratio and minimization of wall thickness difference which are significant optimization objectives. Residual wall thickness was calculated by the computational fluid dynamics (CFD) method. The response surface methodology (RSM) model, radial basis function (RBF) neural network, and Kriging model were employed to map the relationship between process parameters and hollowed core ratio, and wall thickness difference. Based on the comparison assessments of the three surrogate models, multiobjective optimization of hollowed core ratio and wall thickness difference for cooling water pipe by integrating design of experiment (DOE) of optimized Latin hypercubes (Opt LHS), RBF neural network, and particle swarm optimization (PSO) algorithm was studied. The research results showed that short shot size, water pressure, and melt temperature were the most important process parameters affecting hollowed core ratio, while the effects of delay time and mold temperature were little. By the confirmation experiments for the best solution resulted from the Pareto frontier, the relative errors of hollowed core ratio and wall thickness are 2.2% and 3.0%, respectively. It demonstrated that the proposed hybrid optimization methodology could increase hollowed core ratio and decrease wall thickness difference during the WAIM process.

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“…This can significantly reduce the experimental costs. Su et al [ 13 ] used the Kriging model to predict the stiffness of an injection-molded short-fiber-reinforced composite. The Kriging model demonstrated high accuracy when compared to the experimental results.…”

Section: Introductionmentioning

confidence: 99%

“…shows a comparison between the predicted and experimental values for the strength of composite laminates with varying sizes of resin-missing defects. The predicted strength of the composite laminate with defects of 5.3%, 8.0%, 10.7%,13.3%, and 16.7% is 1499.81 MPa, 1490.27 MPa, 1485.93 MPa, 1474.36 MPa, and 1456.79 MPa, respectively. The error with in experiment is 4.50%, 4.93%, 1.54%, 1.43%, and 5.20%, respectively.…”

mentioning

confidence: 99%

A Fast and Efficient Approach to Strength Prediction for Carbon/Epoxy Composites with Resin-Missing Defects

Li,

Zhu

2024

Polymers

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A novel method is proposed to quickly predict the tensile strength of carbon/epoxy composites with resin-missing defects. The univariate Chebyshev prediction model (UCPM) was developed using the dimension reduction method and Chebyshev polynomials. To enhance the computational efficiency and reduce the manual modeling workload, a parameterization script for the finite element model was established using Python during the model construction process. To validate the model, specimens with different defect sizes were prepared using the vacuum assistant resin infusion (VARI) process, the mechanical properties of the specimens were tested, and the model predictions were analyzed in comparison with the experimental results. Additionally, the impact of the order (second–ninth) on the predictive accuracy of the UCPM was examined, and the performance of the model was evaluated using statistical errors. The results demonstrate that the prediction model has a high prediction accuracy, with a maximum prediction error of 5.20% compared to the experimental results. A low order resulted in underfitting, while increasing the order can improve the prediction accuracy of the UCPM. However, if the order is too high, overfitting may occur, leading to a decrease in the prediction accuracy.

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Kriging‐based orthotropic closure for flow‐induced fiber orientation and the part stiffness predictions with experimental investigation

Cited by 5 publications

References 23 publications

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

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

Study of Residual Wall Thickness and Multiobjective Optimization for Process Parameters of Water-Assisted Injection Molding

A Fast and Efficient Approach to Strength Prediction for Carbon/Epoxy Composites with Resin-Missing Defects

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