基于碳纳米纸传感器和深度学习的碳纤维复合材料损伤监测

Damage monitoring of carbon fiber composite material based on carbon nanopaper sensors and deep learning

纤维增强树脂基复合材料损伤机理复杂,为保证其长期稳定应用,需要采用先进的健康监测技术对其进行损伤监测。基于碳纳米纸传感器可灵敏监测电阻变化,对碳纤维增强聚合物(carbon fibre reinforced polymer composite,CFRP)复合材料进行冲击损伤监测,并设计出一套基于人工神经网络(artificial neural network,ANN)深度学习算法的损伤监测系统。通过数据分析可知,该系统可长期有效地监测CFRP的损伤发生与位置预测,且损伤位置精确度高达92%。该损伤监测系统可实现对复合材料健康状况的评估。

The damage mechanism of fiber reinforced resin matrix composites is complex.To ensure long-term stable application,advanced health monitoring technology must be used to monitor the dam‐age.A sensor based on carbon nanopaper can sensitively monitor resistance changes and impact damage on carbon fiber reinforced polymer(CFRP)composite.A damage monitoring system based on an artifi‐cial neural network(ANN)deep-learning algorithm was designed.Through data analysis,the system could effectively monitor the occurrence and location of CFRP damage for a long time,and the damage location accuracy was as high as 92%.It can be inferred that the damage monitoring system can evalu‐ate the health status of composite materials.