Purpose Low-velocity impacts may cause barely visible internal damage in carbon fiber-reinforced polymer (CFRP) laminates, which can significantly reduce the load-carrying capacity of Type IV hydrogen storage cylinders. This study aims to propose an acoustic-emission (AE) sensing framework to quantitatively evaluate low-velocity impact damage in CFRP laminates relevant to Type IV cylinder structures, and to establish severity grading criteria based on an interpretable scalar indicator. Design/methodology/approach Drop-weight impact tests were conducted from 10–80 J while AE signals were continuously recorded. AE features were extracted using empirical mode decomposition (EMD) and principal component analysis (PCA). A hybrid Convolutional Neural Network–Long Short-Term Memory (CNN–LSTM) classifier was trained to identify damage-related AE signals, and wavelet entropy was calculated to quantify impact damage severity. Nonlinear ultrasonic testing was additionally used to validate damage progression. Findings The CNN–LSTM model achieved 93.3% classification accuracy for AE signals associated with matrix cracking, delamination and fiber fracture. Wavelet entropy showed strong correlation with impact damage severity and identified three damage stages. Two critical thresholds were found: wavelet entropy 0.275 (transition to moderate damage) and 0.55 (transition to severe damage), corresponding to impact energies of 40 and 60 J, respectively. Originality/value This study links AE signal characteristics to impact damage severity through an interpretable scalar metric (wavelet entropy) and provides quantitative thresholds (0.275 and 0.55) for three-stage damage grading. It further combines EMD–PCA feature extraction with a CNN–LSTM classifier (93.3% accuracy) to identify damage-related AE signals, and validates damage progression using nonlinear ultrasonic testing. The framework offers a practical sensing strategy for composite pressure-vessel monitoring.
Long et al. (Thu,) studied this question.