Multiscale collaborative optimization for the thermochemical and thermomechanical cure process during composite manufacture

To reconcile the contradiction between the improvement of carbon fiber reinforced resin matrix (CFRP) composite performance and the increase of manufacturing cost, this paper proposes a collaborative optimization strategy for the cure process during the composite manufacture to reduce both the process-induced defects and the process time. Considering the multiscale characteristics of the composites, the temperature gradient is calculated by the macroscale laminate model through the nonlinear heat transfer thermochemical analysis, and the residual stresses are obtained by the representative volume element (RVE) microscale model, which involves the viscoelasticity, thermal expansion and cure shrinkage of the constituents during the thermomechanical analysis. The multi-objective optimization is implemented by an interface which combines the finite element based cure process analysis with the non-dominated sorting genetic algorithm-II (NSGA-II). The results show the proposed optimization strategy can significantly reduce the maximum temperature gradient, the maximum residual stress and the process time simultaneously. Besides, the self-organizing map (SOM) obtained from the Pareto front clarifies the relationship between the design variables and the objectives.

Automated summary

In this paper, a collaborative optimization strategy is proposed to reconcile the contradiction between the improvement of carbon fiber reinforced resin matrix composite performance and the increase of manufacturing cost. The strategy aims to reduce both process-induced defects and process time. By considering the multiscale characteristics of the composites and implementing a multi-objective optimization, the proposed strategy successfully reduces the maximum temperature gradient, maximum residual stress, and process time simultaneously.