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Prediction and Optimization of Cure Cycle of Thick Fiber Reinforced Composite Parts Using Artificial Neural Networks

Eghbal Jahromi, Parisa | 2009

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  1. Type of Document: M.Sc. Thesis
  2. Language: Farsi
  3. Document No: 39385 (06)
  4. University: Sharif University of Technology
  5. Department: Chemical and Petroleum Engineering
  6. Advisor(s): Shojaei, Akbar; Pishvaie, Mahmoud Reza
  7. Abstract:
  8. The Curing of thick thermoset parts of composites Experience substantial temperature overshoot, especially at the center of those thick parts and large temperature gradient exists through the whole part due to large amount of released heat, thickness and low conductivity of the composite.this leads to non-uniformity of cure, residual stress and consequently composite cracks and possibly degradation of the polymer. In this thesis, first a thick cubic geometery of glass/epoxy composite is modeled with the help ofa commercial CFd packageand sensitivity to different effective parameters of this process is investigated with the help of this model.This model is then substituted by a trained dynamic artificial neural networkto speed up the modelling process, purposed for repetitious use during optimization process. To improve the composite propertiesoptimized multi- inear-stage cure cycles are developed by minimizing the objective function and applying constraints in which he uniformity of cure and rectification of the flaws are considered.These curing cycles lead to a relatively well cured uniformcomposire, with practically no defects imn its properties in a relatively rapid process
  9. Keywords:
  10. Optimization ; Induration ; Computational Fluid Dynamics (CFD) ; Artificial Neural Network ; Dynamic Neural Network

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