The formation of defects during the lay-up of flat and curved laminates using AFP is very likely during manufacturing and greatly propagates into new defects during subsequent RTM, introducing large uncertainties in the process and affecting the final structural properties. AFP typical defects are gaps and overlaps between tapes, and stability of the tape leading to wrinkles and bubbles. In the following RTM process after AFP, resin flow front is affected by these defects, showing unpredicted behavior which can lead to pores, air bubbles and voids or even non-impregnated areas. The impact of these manufacturing defects on the mechanical performance of composite structures needs to be quantified for reliable and safe designs. The quantification of the effect of the manufacturing defects on the mechanical performance is typically addressed by means of experimental test campaigns, however, the time and cost needed to perform them are very expensive. In this work, we present an approach to numerically quantify the effect on the mechanical properties of the presence of manufacturing defects. A non-deterministic simulation approach is presented to account for the uncertainties propagated along the manufacturing chain and the presence of manufacturing defects. Several simulations will be executed with different model parameters (material variability and/or the presence of defects), based on parametrized models (Python scripts) to devise a methodology for uncertainty quantification and management related to product mechanical performance and the determination of knock-down factors associated to the presence of defects. The methodology presented is integrated in a parallel computational workflow called PyCOMPSs [1] and using Alya multiphysics code [2] as the main simulator within the framework of the CAELESTIS Horizon Europe project. The approach will be challenged to obtain the knock-down factor on the open hole strength of an aeronautical carbon-epoxy laminate. REFERENCES [1] Tejedor E, Becerra Y, Alomar G, et al. PyCOMPSs: Parallel computational workflows in Python. The International Journal of High Performance Computing Applications. 2017;31(1):66-82. doi:10.1177/1094342015594678 [2] Vázquez, M., Houzeaux, G., Koric, S., Artigues, A., Aguado-Sierra, J., Arís, R., Mira, D., Calmet, H., Cucchietti, F., Owen, H., Taha, A., Burness, E.D., Cela, J.M., Valero, M., 2016. Alya: Multiphysics engineering simulation toward exascale. Journal of Computational Scienc