Particle filter‐based prognostics for composite curing process

Abstract Process‐induced deformation (PID) arises in thermoset parts due to internal residual stress developed from their anisotropic properties, resulting in distortions. While passive numerical manufacturing control exists, active manufacturing control is crucial for enhancing the manufacturing process. The work focuses on diagnosing the polymerization reaction, known as the curing process, to consider the influence of uncertainties in thermal loading conditions on the behavior of cure kinetics. This is achieved using a Particle Filter approach, wherein a posterior distribution of cure evolution is recursively approximated based on observed measurements from characterization tests. The algorithm is designed to simultaneously perform the diagnosis and prognosis of the Degree of Cure and PID. This approach adopts the augmented cure formulation to address various scenarios with uncertainties in thermal loading conditions. It offers the advantage of providing comparable PID predictions with minimal computational costs. C‐shaped thermoset parts made of epoxy/carbon fibers with varying thicknesses are cured using the Manufacturing Recommended Curing Cycle, and the predictions with the developed algorithm are validated against experimental measures. Upon validation, the converged prognosis capability of the Particle Filter model is employed to assess the impact of thermal loading uncertainty on cure profiles, which, in turn, affects the final PIDs outcome. Highlights A Bayesian sampling approach enables the estimation of cure kinetics parameters. The estimated stochastic parameters forecast the process‐induced deformations. The augmented Degree of Cure accounts for uncertainties linked to thermal loadings. Analysis on AS4/8552 C‐shaped parts shows the cure kinetics impact. The framework reduces the computational costs required for active control.