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A Parametric Study of Epoxy-Bonded CF/QF-BMI Composite Joints Using a Method Combining RBF Neural Networks and

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This study optimized epoxy-bonded joints between carbon-fiber and quartz-fiber composites using machine learning and a genetic algorithm. The novel design methodology enhanced tensile and shear strength by over 16% and 11%, respectively.

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NSGA-II algorithmRBF neuron machine learningcarbon-fiber-reinforced bismaleimide compositeepoxy-bonded jointfinite element simulation modelquartz-fiber-reinforced bismaleimide compositetensile and shear strength

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Area of Science:

  • Materials Science
  • Composite Materials Engineering
  • Aerospace Engineering

Background:

  • Next-generation aviation equipment requires structure-function integrated composite materials.
  • The bonding zone design critically impacts the service performance of epoxy-bonded carbon-fiber-reinforced bismaleimide (CF-BMI) and quartz-fiber-reinforced bismaleimide (QF-BMI) composites.
  • Understanding the influence of bonding area size on mechanical properties is crucial for optimizing joint performance.

Purpose of the Study:

  • To investigate the effect of bonding area size on the mechanical properties of epoxy-bonded CF/QF-BMI composites.
  • To propose and validate a novel design methodology combining radial basis function (RBF) neuron machine learning and the NSGA-II algorithm for enhancing mechanical properties.
  • To optimize the structural parameters of the bonding zone for improved tensile and shear strength.

Main Methods:

  • Established a finite element simulation model incorporating 3D Hashin criteria and cohesion, validated through experimental testing.
  • Trained an RBF neuron model using finite element simulation data on tensile and shear strength for various adhesive layer parameters.
  • Employed the NSGA-II algorithm for multi-objective parameter optimization of the surrogate RBF model.

Main Results:

  • Demonstrated high consistency between finite element simulation results and experimental outcomes for the epoxy-bonded CF/QF-BMI composite joint.
  • Observed similar stress distributions in adhesive layers across different structural parameters, but varying dimensions led to distinct failure modes.
  • The trained RBF model achieved prediction errors within 2.21%, accurately reflecting service performance.
  • The optimized joint design showed a 16.1% increase in tensile strength and an 11.2% increase in shear strength.

Conclusions:

  • The developed finite element model and RBF-trained surrogate model accurately predict the mechanical behavior of epoxy-bonded CF/QF-BMI joints.
  • The combination of RBF machine learning and NSGA-II algorithm offers an effective approach for optimizing composite joint designs.
  • The optimized bonding zone design significantly enhances the tensile and shear strength, meeting advanced aviation requirements.