Axel Spickenheuer

TL;DR: The effect of noise on a PNN based uncertainty quantification algorithm is explored and the convergence of the proposed algorithm for stochastic natural frequency analysis of composite plates is verified and validated with original finite element method (FEM).

TL;DR: In this article, a numerical and experimental investigation on unnotched and open-hole tensile characteristics of fiber-steered variable-axial composite laminates was conducted, where the fiber path was obtained from an optimization framework considering manufacturing characteristics of the Tailored Fiber Placement (TFP) process.

TL;DR: The stochastic natural frequency for laminated composite plates is presented by using artificial neural network (ANN) model and the ANN model is employed as a surrogate and is trained by using supervised learning.

TL;DR: The current work on optimization of the linear buckling behavior of variable-axial (VA) shells shows both the potential of using VA-configurations to exploit their tailoring ability and the capabilities of the current optimization framework to improve and optimize the behavior of VA structures.

TL;DR: In this paper, a bottom up surrogate based approach is employed to quantify the variability in free vibration responses of composite cantilever plates due to uncertainty in ply orientation angle, elastic modulus and mass density.