Print Email Facebook Twitter Non-destructive strength prediction of composite laminates utilizing deep learning and the stochastic finite element methods Title Non-destructive strength prediction of composite laminates utilizing deep learning and the stochastic finite element methods Author Nastos Konstantopoulos, C. (TU Delft Structural Integrity & Composites) Komninos, P. (TU Delft Structural Integrity & Composites) Zarouchas, D. (TU Delft Structural Integrity & Composites) Date 2023 Abstract A hybrid methodology based on numerical and non-destructive experimental schemes, which is able to predict the structural level strength of composite laminates is proposed on the current work. The main objective is to predict the strength by substituting the up to failure experiments with non-destructive experiments where the investigated specimen is loaded up to 20% of its maximum load. A significant gap exists between the 20% and the 100% load which is proposed to be treated by high fidelity physics-based numerical models, deep learning techniques, and non-catastrophic experiments. Thus, a deep learning algorithm is developed, based on the convolutional neural networks and trained by probabilistic failure analysis datasets which result from the utilization of the stochastic finite element method. Also, the Monte Carlo dropout technique is embedded into the developed convolutional neural network to estimate the uncertainty induced by the investigated variations between the simulated and experimental data. The current paper provides a thorough description of the proposed methodology and a practical example which demonstrates the validity of the method. Subject Composite structuresDeep learningProbabilistic modelsStochastic finite element methodStrength predictionUncertainty To reference this document use: http://resolver.tudelft.nl/uuid:5414c013-b6cf-411b-a155-bad0c7a5232a DOI https://doi.org/10.1016/j.compstruct.2023.116815 ISSN 0263-8223 Source Composite Structures, 311 Part of collection Institutional Repository Document type journal article Rights © 2023 C. Nastos Konstantopoulos, P. Komninos, D. Zarouchas Files PDF 1_s2.0_S0263822323001599_main.pdf 3.04 MB Close viewer /islandora/object/uuid:5414c013-b6cf-411b-a155-bad0c7a5232a/datastream/OBJ/view