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Modeling and Comparative Study on Cure Kinetics for CFRP: Autocatalytic vs. Neural Network vs. Angle

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  • 1School of Advanced Manufacturing, Nanchang University, Nanchang 330031, China.

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A new angle-enhanced radial basis function (RBF) model improves carbon fiber reinforced polymer (CFRP) composite cure kinetics prediction. This data-driven approach offers better accuracy and robustness than traditional models for manufacturing process control.

Keywords:
angle information-enhanced radial basis functioncarbon fiber reinforced polymercure kinetics model

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Area of Science:

  • Materials Science
  • Polymer Chemistry
  • Computational Modeling

Background:

  • Carbon fiber reinforced polymer (CFRP) components demand precise curing for quality assurance.
  • Traditional cure kinetics models (phenomenological) struggle with nonlinearity and diverse data.
  • Neural networks face robustness challenges due to hyperparameter complexity and data dependency.

Purpose of the Study:

  • To develop a novel, robust machine learning model for CFRP cure kinetics prediction.
  • To address the limitations of existing phenomenological models and neural networks in CFRP composite processing.
  • To enhance the accuracy and stability of cure kinetics modeling using a data-driven approach.

Main Methods:

  • Proposed a novel angle information-enhanced radial basis function (RBF) model.
  • Integrated Euclidean distance and angular relationships for improved data point analysis.
  • Validated the model against an autocatalytic model and a neural network using dynamic DSC data from T700/2626 epoxy resin at various heating rates.

Main Results:

  • The angle-enhanced RBF model demonstrated superior prediction stability and accuracy.
  • Achieved a balance between accuracy, efficiency, and robustness in cure kinetics prediction.
  • Outperformed traditional models and neural networks in predicting CFRP composite curing behavior.

Conclusions:

  • The angle-enhanced RBF model provides a reliable, data-driven alternative for CFRP cure kinetics.
  • This approach facilitates better manufacturing process control by enabling precise prediction.
  • The model reduces the need for extensive data and complex hyperparameter tuning.