Instant flow distribution network optimization in liquid composite molding using deep reinforcement learning
Instant flow distribution network optimization in liquid composite molding using deep reinforcement learning
复制标题
使用深度强化学习在液体复合材料成型中进行即时流量分配网络优化
DOI：
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发表时间：
2022
影响因子：
8.3
通讯作者：
S. Chauhan
中科院分区：
文献类型：
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作者：
Martin Szarski;S. Chauhan
碳纤维增强塑料 (CFRP) 制造周期是航空航天制造商生产率和成本的主要驱动因素。在真空辅助树脂传递模塑 (VARTM) 中，液体热固性树脂在真空压力下注入干碳增强材料中，设计树脂分配网络以最大限度地缩短填充时间，同时确保预成型件完全充满树脂，这对于实现可接受的质量和周期时间至关重要。航空航天复合材料中复杂的树脂分配网络增加了对快速、优化的虚拟设计反馈的需求。从强化学习的角度来构建流动介质放置问题，我们使用基于 3D 有限元的干碳预成型件中树脂流动过程模型来训练深度神经网络代理。我们的代理学习将流动介质放置在薄层压板上，以避免树脂不足并减少总输注时间。由于代理在各种薄层压板几何形状的培训过程中获得的知识，当呈现新的薄层压板几何形状时，它能够在不到一秒的时间内提出良好的流介质布局。在具有复杂 12 维流媒体网络的真实航空航天零件上，我们证明与专家设计的放置相比，我们的方法可将填充时间缩短 32%，同时保持相同的填充质量。
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.