Ultrasonic lamination defects detection of carbon fiber composite plates based on multilevel LSTM

Abstract

During the production of carbon fiber composites (CFC), various forming methods and complex processes can introduce different types of defects, with lamination defects being a major concern. In this paper, we propose a multilevel Long Short-Term Memory (LSTM) neural network combined with ultrasonic detection to identify the lamination defects in carbon fiber composites. Unlike conventional ultrasonic detection that requires multiple sets of probes, this method only employs a one-to-one transmission and reception mode. The COMSOL-Multiphysics finite element software is utilized to simulate the ultrasonic transmission and generate the necessary ultrasonic data. By incorporating multiple levels of learning, the accuracy and convergence of the traditional LSTM can be obviously enhanced. This approach uses ultrasound waveform data collected from a single set of probes to predict the locations and sizes of lamination defects. Based on fewer than 10,000 datasets where each dataset represents a waveform, the numerical results demonstrate a prediction accuracy of over 90% for defect position and size. Moreover, the multilevel LSTM method exhibits convergence, and incorporating more data can further promote the prediction accuracy. This method offers a time-saving, labor-saving, and cost-effective solution for detecting and analyzing defects in CFC.