Multi-objective curing optimization of carbon fiber composite materials using data assimilation and localized heating

In this study, we estimate the curing state of an entire carbon-fiber-reinforced plastic laminate by using data assimilation that combines observation values and simulation. We used curing simulation based on an estimated thermal conductivity distribution to perform multi-objective optimization of a localized heating method to minimize the cure degree inhomogeneity and curing time. We found from the values of objective functions that multi-objective optimization, using curing simulation based on data assimilation, is effective for minimizing the cure degree inhomogeneity and curing time. In addition, it was verified that the solution set obtained by data assimilation is superior to that without data assimilation by using the Hypervolume method. We also used self organizing maps to visualize the multi-objective optimization results of the models that perform the internal estimation using data assimilation, in order to show that the relationships between the objective functions and the design variables can be clarified.