Modeling and Comparative Study on Cure Kinetics for CFRP: Autocatalytic vs. Neural Network vs. Angle Information-Enhanced RBF Models Article Swipe
Xintong Wu
,
Linman Wei
,
Ming Zhang
,
Zhongling Liu
,
Bin Xiao
,
Xiaobo Yang
,
Zan Yang
,
Xintong Wu
,
Linman Wei
,
Ming Zhang
,
Zhongling Liu
,
Bin Xiao
,
Xiaobo Yang
,
Zan Yang
·
YOU?
·
· 2025
· Open Access
·
· DOI: https://doi.org/10.3390/polym17223059
YOU?
·
· 2025
· Open Access
·
· DOI: https://doi.org/10.3390/polym17223059
Carbon fiber reinforced polymer (CFRP) components require precise curing process control to ensure quality, but traditional phenomenological cure kinetics models face limitations in handling nonlinearity and data diversity. This study addresses the challenges in modeling the cure kinetics of carbon fiber reinforced polymer (CFRP) composites, where traditional phenomenological models lack generalizability and neural networks suffer from robustness issues due to their numerous hyperparameters and data dependency. To overcome these limitations, a novel machine learning model called the angle information-enhanced radial basis function (RBF) model is proposed, which integrates both Euclidean distance and angular relationships between data points to improve prediction stability and accuracy. The performance of this machine learning approach is systematically compared against an autocatalytic model and a neural network using dynamic DSC data from T700/2626 epoxy resin at multiple heating rates. The angle-enhanced RBF model balances accuracy, efficiency, and robustness, offering a reliable data-driven alternative for CFRP cure kinetics prediction without requiring extensive data or complex hyperparameter optimization, thus facilitating better process control in manufacturing.
Related Topics
Concepts
Artificial neural network
Hyperparameter
Computer science
Artificial intelligence
Materials science
Robustness (evolution)
Phenomenological model
Carbon fiber reinforced polymer
Experimental data
Radial basis function
Machine learning
Data-driven
Extrapolation
Biological system
Data modeling
Curing (chemistry)
Nonlinear system
Process control
Curve fitting
Generalizability theory
Stability (learning theory)
Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.3390/polym17223059
- https://www.mdpi.com/2073-4360/17/22/3059/pdf?version=1763476077
- OA Status
- gold
- OpenAlex ID
- https://openalex.org/W7105915335
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W7105915335Canonical identifier for this work in OpenAlex
- DOI
-
https://doi.org/10.3390/polym17223059Digital Object Identifier
- Title
-
Modeling and Comparative Study on Cure Kinetics for CFRP: Autocatalytic vs. Neural Network vs. Angle Information-Enhanced RBF ModelsWork title
- Type
-
articleOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2025Year of publication
- Publication date
-
2025-11-18Full publication date if available
- Authors
-
Xintong Wu, Linman Wei, Ming Zhang, Zhongling Liu, Bin Xiao, Xiaobo Yang, Zan Yang, Xintong Wu, Linman Wei, Ming Zhang, Zhongling Liu, Bin Xiao, Xiaobo Yang, Zan YangList of authors in order
- Landing page
-
https://doi.org/10.3390/polym17223059Publisher landing page
- PDF URL
-
https://www.mdpi.com/2073-4360/17/22/3059/pdf?version=1763476077Direct link to full text PDF
- Open access
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YesWhether a free full text is available
- OA status
-
goldOpen access status per OpenAlex
- OA URL
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https://www.mdpi.com/2073-4360/17/22/3059/pdf?version=1763476077Direct OA link when available
- Concepts
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Artificial neural network, Hyperparameter, Computer science, Artificial intelligence, Materials science, Robustness (evolution), Phenomenological model, Carbon fiber reinforced polymer, Experimental data, Radial basis function, Machine learning, Data-driven, Extrapolation, Biological system, Data modeling, Curing (chemistry), Nonlinear system, Process control, Curve fitting, Generalizability theory, Stability (learning theory)Top concepts (fields/topics) attached by OpenAlex
- Cited by
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0Total citation count in OpenAlex
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