Although automation technology is developing rapidly, setup and control of nonwoven cards is still done manually and based on the experience of the machine user. In Germany alone this leads to scrap production worth 50 Mio. € per year. Aim of the Easy Nonwoven 4.0 project is to develop a cost/effort based setting aid for the most important drive speeds of nonwoven cards. The goal will be achieved by an improving optical inspection systems placed directly behind the card doffer, a measuring and automation system, which will collect production and product data and a multi-criteria optimisation routine. The systems have been installed on a pilot line and an industrial production line. The gathered big data has successfully been used to model the quality parameters and energy consumption based on the machine settings, material parameters and surrounding conditions. The neural networks used for the modeling are precise enought to simulate the carding process in a multicriteria optimization.