Gadinger, MarcMarcGadingerDeutschmann, TorbenTorbenDeutschmannKrause, DieterDieterKrauseWartzack, SandroSandroWartzack2025-11-112025-11-112025-08-27Proceedings of the Design Society 5: 209-218 (2025)https://hdl.handle.net/11420/58532Lightweight design is critical for improving the efficiency and sustainability of engineering applications. Laminated composites, with their high strength-to-weight ratio and tailored material properties, play a key role but introduce interlaminar stresses, particularly near free edges where delamination failures often occur. Addressing these stresses typically requires computationally expensive 3D finite element simulations, limiting their use in early design stages. This study presents a machine learning approach using Gaussian process regression and artificial neural networks to efficiently predict interlaminar stresses based on in-plane stress data from shell FE simulations. Achieving high predictive accuracy, this method enables cost-effective, early-stage composite design optimization under complex loading scenarios.en2732-527X2025209218Cambridge University Presshttps://creativecommons.org/licenses/by-nc-nd/4.0/lightweight designmachine learning, simulationcomposite materialsearly design phasesTechnology::600: TechnologyJournal Articlehttps://doi.org/10.15480/882.1610510.1017/pds.2025.1003510.15480/882.16105Journal Article