Analysis of cure kinetics of CFRP composites molding process using incremental thermochemical information aggregation networks

Abstract

This work focuses on addressing the pain points of poor generalization performance and difficulty in continuous learning that exist in the phenomenological and neural network surrogate models. Therefore, this study proposes a lightweight adaptive thermochemical information aggregation networks (ATANets) to overcome the gradient conflict challenge, and combines the generative knowledge distillation (GKD) algorithm to compress the model to capture finer-grained and enriched information on kinetics behaviors, which yields a thermochemical feature information extraction networks (FENets) with incremental learning capability. The experimental results demonstrated that as the complexity of the learning task deepened, the FENets models obtained by incremental training with ATANets and GKD still had excellent continuous learning capability, with the relative variations of the coefficients of determination and the mean square error being smaller than 6.7 × 10−3 and 0.3860 × 10−3, respectively. Meanwhile, the accurate characterization of cure kinetics behaviors was achieved in the thermochemical coupling analysis of CFRP, with the maximum values of the average and maximum temperature differences of 0.0176 °C and 0.2538 °C, respectively. Overall results show that our proposed incremental model is remarkably preferable to existing models and is beneficial in promoting the widespread reuse of the existing knowledge of cure kinetics behavior of resins in this domain.