English(EN) Online Gate-Driven Flow Control in Resin Transfer Moulding Using a Neural-Network Surrogate

神经网络优化树脂传递模塑流动控制

作者 PulseAugur 编辑部 · [1 个来源] · 2026-09-01 04:00

研究人员开发了一种新颖的树脂传递模塑控制策略，该策略利用神经网络代理模型来优化辅助进料口压力。该方法旨在通过防止树脂过早到达排气口来确保纤维预制件的完全浸润，从而减少干斑的形成。该方法结合了用于跟踪估计的卡尔曼滤波和用于近似复杂有限元模型的神经网络，在分叉几何形状上显示出填充效率的显著提高。 AI

影响这项研究通过改进复合材料生产中树脂流动的控制，可能导致更高效的制造过程。

排序理由该集群包含一篇学术论文，详细介绍了使用人工智能优化工业过程的新方法。[lever_c_demoted from research: ic=1 ai=0.7]

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arXiv stat.ML TIER_1 English(EN) · Nicholas Wright, Oliver Maclaren, Piaras Kelly, Suresh Advani, Ruanui Nicholson · 2026-09-01 04:00

使用神经网络代理的在线门驱动树脂传递模塑流动控制

arXiv:2608.29521v1 Announce Type: cross Abstract: In resin transfer moulding, complete saturation of the fibre preform is necessary before the resin front reaches the outlet vent(s), to prevent dry-spot formation. In practice, the flow front rarely advances uniformly due to race-…