工艺与制造英文2022被引 8
基于压力传感器的神经网络辅助树脂传递模塑流动前沿估算方法
A neural-network-assisted method for flow-front estimation in resin transfer molding using pressure sensors
Junhong Zhu, David Droste, Adli Dimassi, Axel S Herrmann · Faserinstitut Bremen e.V., Bremen, Germany
摘要整理
树脂传递模塑（RTM）工艺在复合材料制造中具有显著优势，但制件质量对预成型体浸渍过程中的材料和工艺变化敏感。为提高工艺稳健性并实现更好的过程控制，本研究提出了一种基于传感系统与神经网络模型相结合的树脂流动监测方法，该方法可轻松集成到通用RTM工艺中。利用分布在模具表面的有限数量压力传感器提供的压力数据，该方法能够预测任意浸渍时刻的流动前沿分布规律。训练数据集通过基于物理的数值模拟生成。考虑到不确定条件引起的渗透率变化，采用随机变化对渗透率张量进行建模。网络参数通过试错法获得。研究表明，传感器布置方案和数据集规模是模型的敏感因素。最后，通过数值求解对预测结果进行了验证。该方法可用于防止气孔形成，改善最终制件质量。
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