Bayesian optimization-driven exploration of process–structure–property relationship for polyethylene composite films

Abstract The mechanical properties of polymers depend on their processing conditions. However, process optimization often suffers from complex parameter space and incomplete understanding of the structure-property relationship. This study fabricates and optimizes polyethylene (PE)-thermally reduced graphene oxide (TrGO) composite films and investigates how drawing temperature and drawing ratio affect their tensile strength (TS) and elongation-at-break (EAB). A multi-objective Bayesian optimization (MOBO) framework was introduced to explore the fabrication parameter space to identify the Pareto front between TS and EAB. Structural analyses reveal that drawing increases the molecular orientation, crystallinity and crystallite size in the films, while a high drawing temperature promotes preferential crystallization along the (200) plane. The combined effects of drawing ratio and drawing temperature collectively contribute to the enhanced TS of the films. This study establishes a clear process–structure–property relationship in PE composite films and demonstrates an efficient strategy for polymer-based composite materials design.