Collaborative multi‐scale optimization of molding processes for thermosetting/quartz fiber composites based on balancing between efficiencies and mechanical properties

In terms of the interplay between manufacturing cost and quality during the molding of fiber reinforced thermosetting composites, this study proposes a multi‐scale collaborative optimization strategy aimed at shortening molding time and reducing the diminishing defects generated during the process. Macro‐scale thermochemical and micro‐scale thermomechanical calculations are conducted on the composite molding to analyze the origins of temperature gradients and residual stresses. Then, the curing cycle, temperature gradient in the macro‐scale model, and residual stresses in the micro‐scale model are optimized by combining finite element analysis with the Non‐dominated Sorting Genetic Algorithm II. Several curing processes within the Pareto front were selected, and their impact on compressive performance was assessed through experimental analysis. Compared to the standard curing process (SCP), the optimal curing process exhibited a 41.4% reduction in maximum temperature gradient (∆ T max ), a 25.2% de c rease in effective residual stress , and a 20.0% increase in compressive strength. Multi‐scale collaborative optimization strategies are integral to the advancement of production and application of fiber‐reinforced thermoset composites. Multi‐scale collaborative optimization strategies are integral to the advancement of production and application of fiber‐reinforced thermoset composites. Highlights Investigated of interactions among components in composites molding. Optimization of composite molding process with Non‐dominated Sorting Genetic Algorithm II and finite element. Examined the impact of composite molding process on compressive performance.