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Prediction of shape distortions in thermosetting composite parts using neural network interfaced visco-elastic constitutive model
The work aims to enhance the capabilities of a Finite Element tool, specifically related to a rheological thermo-chemo-viscoelastic constitutive model. This enhancement is intended to improve the tool’s ability to predict the distortions in composite parts caused by the polymerization of the thermoset composite matrix. These distortions occur due to internal residual stress generated by the inherent anisotropic properties of the thermoset composite material, including coefficients of thermal expansion and chemical shrinkage. The research work’s improvement is tied to the precise modelling of curing behaviour, which literature acknowledges as having a significant impact on manufacturing defects. In order to accommodate the influence of curing behaviour on various process variables—specifically, different thermal loading rates—a neural network model is implemented as an alternative to a standard diffusion cure-kinetics model. The neural network model is trained using Differential Scanning Calorimetry data and is integrated with the classical visco-elastic constitutive model to more accurately predict the progression of distinct thermoset resin states. This transition between cure states is assessed using two cure state variables: the degree of cure and the glass transition temperature. The enhanced predictions of state transitions lead to accurate assessments of internal residual stresses, especially when dealing with thick components subjected to thermal fluctuations. The anisotropic properties of thermoset composites, crucial for numerical analysis, are captured at various stages of cure. Ultimately, this methodology is employed to compare process-induced defects in the case study of the Z-shaped carbon/epoxy woven part, and the defects closely align with experimental measurements.
Prediction of shape distortions in thermosetting composite parts using neural network interfaced visco-elastic constitutive model
The work aims to enhance the capabilities of a Finite Element tool, specifically related to a rheological thermo-chemo-viscoelastic constitutive model. This enhancement is intended to improve the tool’s ability to predict the distortions in composite parts caused by the polymerization of the thermoset composite matrix. These distortions occur due to internal residual stress generated by the inherent anisotropic properties of the thermoset composite material, including coefficients of thermal expansion and chemical shrinkage. The research work’s improvement is tied to the precise modelling of curing behaviour, which literature acknowledges as having a significant impact on manufacturing defects. In order to accommodate the influence of curing behaviour on various process variables—specifically, different thermal loading rates—a neural network model is implemented as an alternative to a standard diffusion cure-kinetics model. The neural network model is trained using Differential Scanning Calorimetry data and is integrated with the classical visco-elastic constitutive model to more accurately predict the progression of distinct thermoset resin states. This transition between cure states is assessed using two cure state variables: the degree of cure and the glass transition temperature. The enhanced predictions of state transitions lead to accurate assessments of internal residual stresses, especially when dealing with thick components subjected to thermal fluctuations. The anisotropic properties of thermoset composites, crucial for numerical analysis, are captured at various stages of cure. Ultimately, this methodology is employed to compare process-induced defects in the case study of the Z-shaped carbon/epoxy woven part, and the defects closely align with experimental measurements.
Prediction of shape distortions in thermosetting composite parts using neural network interfaced visco-elastic constitutive model
Balaji A. (Autor:in) / Sbarufatti C. (Autor:in) / Dumas D. (Autor:in) / Parmentier A. (Autor:in) / Pierard O. (Autor:in) / Cadini F. (Autor:in) / Balaji, A. / Sbarufatti, C. / Dumas, D. / Parmentier, A.
01.01.2024
doi:10.1177/00219983241235855
Aufsatz (Zeitschrift)
Elektronische Ressource
Englisch
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