Automotive Smart Manufacturing
DLR and Mercedes Benz Türk
Objective: Improve better manipulation of thermoplastic automotive components coming still on “warm temperature” conditions to avoid deformation of the objects, and thus reduce plastic waste and save energy, by enabling a more efficient production.
Current situation and problem description: Picking in line production of thermoplastic automotive components produce deformation of the products and may generate products out of specifications. In this regard measurement of pressure on the grips or hands is not optimized for picking, as temperature (warmness) of the material changes depending on the external temperature.
How the use case (demonstration scenario) will work: The calculation of the optimum force for the gripper will be calculated via AI based software, which will be developed under the collaboration of consortium partners (sensoring, vision, etc.) and also winner from the software open call. This use case will couple hardware and software winners of open calls with consortium partners providing technology, as well as MBT.
Expected outcome (Envisaged pilot execution or potential solution): An integrated and modular solution for the challenge given in the deformable manipulation of thermoplastic objects; outputs related with experimentation with several gripping and manipulation systems as well as AI based software and vision components that comprise simulation integrating temperature and physical parameters to support better gripping/manipulation of deformable objects.
Expected impact: Improvement and feasibility of robotic hardware, integration of AI based software, as well as improvements on carbon footprint, waste and energy consumption. MB expects to save at least 10% of plastic material used and set new KPIs for carbon footprint and energy, which are not currently specifically measured in this production line.
Information source: vojext.eu
Digital, Manufacturing, Construction.