Abstract
Carbon fiber reinforced polymer (CFRP) has been increasingly used in aviation industry since it significantly enhances the performance of aircraft. However, imperfections inside the CFRP structures pose a threat to aviation safety. Apart from the defect shape and size, flaw depth is crucial to assess the defect severity. In this work, we utilize a laser infrared thermography (LIT) system to inspect an aviation CFRP sheet and adopt a long-short term memory recurrent neural network (LSTM-RNN) to determine the defect depth. Thermographic sequences obtained by LIT are processed using thermographic signal reconstructions (TSR) method. Raw data and TSR processed data are separately used to train and test the LSTM-RNN. Results show that background noises in the original thermal signals can be effectively reduced by the TSR method, which is helpful for the models to learn the signal characteristics. Compared with two traditional methods, recurrent neural network (RNN) and convolutional neural network (CNN), we find the LSTM-RNN outperforms.
Highlights
Active infrared thermography (AIRT) is a non-contact, wide range, and rapid technique [1,2,3,4], which has performed effectively in defects detection of carbon fiber reinforced polymer (CFRP) components
Compared with two traditional methods, recurrent neural network (RNN) and convolutional neural network (CNN), we find the long short term memory (LSTM)-RNN outperforms
Results will show that the LSTM-RNN model trained by data sets from thermographic signal reconstructions (TSR) method has great accuracy in determining the depth of defects inside Carbon fiber reinforced polymer (CFRP) laminates
Summary
Active infrared thermography (AIRT) is a non-contact, wide range, and rapid technique [1,2,3,4], which has performed effectively in defects detection of carbon fiber reinforced polymer (CFRP) components. Internal imperfections of less effusivity appears as an area of higher temperature than surrounding sound materials during the stimulation course This feature can be captured by a thermal camera, making defects detection possible. While thermographic sequences recorded by infrared camera could be regarded as temperature-time sequences of each point on the surface of inspected structure, where internal defects at different depths exhibited different characteristics in their sequences. LIT inspected an CFRP specimen and thermal responses (temperature evolutions) in after-exciting period (cooling phase) will be used as data sets. Results will show that the LSTM-RNN model trained by data sets from TSR method has great accuracy in determining the depth of defects inside CFRP laminates. Compared with two other methods, we will show that the LSTM-RNN method is appealing in infrared thermal sequence processing
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a call
