Model based data-driven optimisation of automated fibre placement set up

The composites manufacturing industry relies heavily on manual hand layup, unsuitable for increasing demands. Automated Fiber Placement (AFP) offers a solution, but setup complexity and extensive trials inflate costs. Digital tools promise to expedite development, but CPU-intensive simulations limit large-scale parameter optimisation. This paper introduces Gaussian Process (GP) models for understanding AFP parameters' relationship with nip-point temperature. The GP emulator streamlines optimisation, offering accurate power curves with minimal simulations. An efficient method to enhance emulator performance through sequential design is also proposed, providing a cost-effective decision tool for AFP optimisation.