Classification of Liquid Ingress in GFRP Honeycomb Based on One-Dimension Sequential Model Using THz-TDS

Honeycomb structure composites are taking an increasing proportion in aircraft manufacturing because of their high strength-to-weight ratio, good fatigue resistance, and low manufacturing cost. However, the hollow structure is very prone to liquid ingress. Here, we report a fast and automatic classification approach for water, alcohol, and oil filled in glass fiber reinforced polymer (GFRP) honeycomb structures through terahertz time-domain spectroscopy (THz-TDS). We propose an improved one-dimensional convolutional neural network (1D-CNN) model, and compared it with long short-term memory (LSTM) and ordinary 1D-CNN models, which are classification networks based on one dimension sequenced signals. The automated liquid classification results show that the LSTM model has the best performance for the time-domain signals, while the improved 1D-CNN model performed best for the frequency-domain signals.

Automated summary

Honeycomb structure composites are widely used in aircraft manufacturing due to their high strength-to-weight ratio, good fatigue resistance, and low manufacturing cost, but the hollow structure is susceptible to liquid ingress. In this study, a fast and automatic liquid classification method for water, alcohol, and oil filled in glass fiber reinforced polymer (GFRP) honeycomb structures was proposed using terahertz time-domain spectroscopy (THz-TDS). An improved one-dimensional convolutional neural network (1D-CNN) model was developed and compared with long short-term memory (LSTM) and ordinary 1D-CNN models. The results showed that the LSTM model performed the best for time-domain signals, while the improved 1D-CNN model had the best performance for frequency-domain signals.