Discretisation-induced stiffening and its impact on CNN-based delamination detection in composite structures

Abstract The structural application of composite materials has intensified in high-performance engineering sectors; however, interlaminar delamination remains one of the most critical damage mechanisms due to its low detectability and complex effects on dynamic response. This work investigates the influence of multiple internal delaminations on the vibration behavior of Carbon Fiber Reinforced Polymer (CFRP) laminated plates and evaluates the feasibility of using Convolutional Neural Networks (CNN) for damage identification from modal data. A ply-level finite element model was developed to generate 1,000 damage scenarios, spanning delaminated areas from $$0.004\%$$ 0.004 % to $$3.10\%$$ 3.10 % , producing 40, 000 modal field images. As a theoretical innovation, this work establishes via the Courant-Fischer theorem that constraint-release delamination models cannot physically increase natural frequencies; any observed positive shifts strictly represent discretisation errors, thereby defining a numerical noise floor. Statistical analysis reveals that mean modal curvature and frequency shifts exhibit statistically indistinguishable correlations with damage severity ( $$r = 0.50$$ r = 0.50 and 0.46, respectively). Furthermore, CNN performance is rigorously benchmarked against novel analytical reference predictors derived from the sampling design. The networks achieved accuracies below 0.20 and $$R^2 < 0.054$$ R 2 < 0.054 , outperforming a baseline majority-class predictor by at most four percentage points. The hold-out predictions exclude overfitting as the cause: in 37 of the 40 classification models the retained network assigns a single class to every test image. The results demonstrate that the CNN’s predictive limitations arise not merely from the physical subtlety of the damage, but because the damage signature is fundamentally eclipsed by the model’s discretisation noise, establishing critical numerical resolution limits for deep learning datasets in structural health monitoring.