Single-Criterion Optimisation with Consideration of Uncertainties of the Composite Multi-Layer Slabs

This paper presents a novel, efficient computational framework for the optimisation of the fundamental frequency of multi-layered composite slabs with consideration of uncertainties. The approach is based on Finite Element Method (FEM) data generation, Deep Neural Network (DNN) surrogate modelling, deterministic optimisation using the genetic algorithm (GA), Morris Sensitivity Analysis (SA), and quantile-based optimisation, including uncertainties and using the GA. Different boundary condition configurations are considered. The surrogate model is trained on FEM-generated samples and subsequently used to replace expensive modal analyses during optimisation, significantly reducing the optimisation evaluation cost for one boundary condition variant. The proposed method achieves near-identical optimal non-dimensional parameter Ω values to those reported in the literature for Bayesian Optimisation (BO), with discrepancies of less than 0.5%. To improve robustness to manufacturing tolerances, an additional uncertainty-aware optimisation is performed, in which model parameters are perturbed with normally distributed noise. By maximising the 5% quantile of the non-dimensional parameter Ω, robust optimal solutions are obtained with minimal loss in performance. Overall, the DNN-GA framework enables fast and accurate optimisation of composite laminates and provides both deterministic and robust design recommendations at a fraction of the computational cost of traditional FEM-based optimisation workflows.