Advanced deep learning model-based impact characterization method for composite laminates

The aim of this study was to develop a structural health monitoring system for smart composite structures through the use of signal processing, deep learning algorithms, and optimization theory. Piezoelectric ribbon sensors were implemented in the preparation of smart composite structures to create a smart composite fabric that can be embedded in composite laminates to enable self-monitoring. A discrete wavelet transform was applied to the impact signals to convert them into input image data for the predictive convolutional neural network-based models. Optimal values of the hyperparameters were derived based on Bayesian optimization theory. Data augmentation was also employed to secure sufficient data for impact characterization model training. Lastly, the performance of each optimized neural network model was investigated by comparing the test errors under each applied condition.

Automated summary

This study aimed to develop a structural health monitoring system for smart composite structures using signal processing, deep learning algorithms, and optimization theory. Piezoelectric ribbon sensors and discrete wavelet transform were utilized for creating smart composite fabric and converting impact signals into input image data. Optimal hyperparameter values were determined based on Bayesian optimization theory, and data augmentation was employed to ensure sufficient data for training impact characterization models. Performance of each optimized neural network model was evaluated by comparing test errors under different conditions.