LINTEL involves companies from Turkey, Austria and Korea in developing an advanced design tool based on artificial intelligence that is aimed to optimize the material and design of the sealing system cycle in the manufacturing process of vehicles. Over the course of 2 years, through a wide range of robust R&D activities, the LINTEL project will lead to 8 key outputs to generate future design methodology applied in cognitive manufacturing : O1: Sealing element compound design data set suitable for machine learning techniques O2: Seal cross-section design data set suitable for machine learning techniques (training, testing, validation); O3: Algorithm developed and verified for compound design data; O4: Algorithm developed and verified for section design data; O5: Simulation model with clear indication of quality of predictions; O6: A software platform incorporating the algorithm, that can allow users to enter target properties and available input information to design optimal product; O7: Platform needs to be easy to use for engineers and designers and deployed on site in correct environment; O8: Validation of the models produced based on experimental design cycles. The following KPIs for LINTEL are set accordingly: KPI1: Reducing the design and performance testing process by 40% in a new compound design process, KPI2: Reduction of 50% of the design, performance-testing process in a new section design process. KPI3: Reducing the duration of the sealing cycle with 15%.