Instant flow distribution network optimization in liquid composite molding using deep reinforcement learning

Abstract

Carbon fibre reinforced plastic (CFRP) manufacturing cycle time is a major driver of production rate and cost for aerospace manufacturers. In vacuum assisted resin transfer molding (VARTM) where liquid thermoset resin is infused into dry carbon reinforcement under vacuum pressure, the design of a resin distribution network to minimize fill time while ensuring the preform is completely full of resin is critical to achieving acceptable quality and cycle time. Complex resin distribution networks in aerospace composites increase the need for quick, optimized virtual design feedback. Framing the problem flow media placement in terms of reinforcement learning, we train a deep neural network agent using a 3D Finite Element based process model of resin flow in dry carbon preforms. Our agent learns to place flow media on thin laminates in order to avoid resin starvation and reduce total infusion time. Due to the knowledge the agent has gained during training on a variety of thin laminate geometries, when presented with a new thin laminate geometry it is able to propose a good flow media layout in less than a second. On a realistic aerospace part with a complex 12-dimensional flow media network, we demonstrate our method reduces fill time by 32% when compared to an expert designed placement, while maintaining the same fill quality.