RECOPRODAS, a TRINITY demonstration

Increasing throughput and quality consistency of manual performed processes inside a high-mix/low-volume job shop environment requires flexible automation capable of automating the recurring tasks that are part of the overall processes happening inside a production cell. Relieving the human operator of repetitive tasks should at the same time increase job satisfaction but this is only achieved when the operator stays in control of what’s happening inside the cell and doesn’t give him/her the feeling that he/she has become part of the ‘machine’. Production at Malmar is therefore in need of A Reconfigurable Cobotic PRODuction Assistant (RECOPRODAS). in accordance with the Quick Response Manufacturing (QRM) self steering teams, the operator in a cell is in control of the cobot and is ‘steering’ the cobot towards the tasks in the production cell where the repetitive and precision skills of the cobot are best suited whilst the operator performs the tasks where more dexterity is required. Creating this RECOPRODAS has been done by providing the cobot with: A mobile platform, dockable at different machines inside a typical sheet metal production cell at Malmar Interface possibilities prepared for communicating with the different machine types in the cell A modular fixture system able to fix workpieces of different sizes and shapes for handling or regrasping A programming approach that is easy-to-use and gives low-level access to programming complex, force sensitive movements. A first prototype of the RECOPRODAS has been developed, realized and tested in the lab at Sirris as well as in the production of Malmar Lithuania. For more info, visit: https://trinityrobotics.eu/use-cases/... and https://www.malmar.be/blocks/zerowaste Check our website to learn more about the TRINITY project: https://trinityrobotics.eu TRINITY: "Digital Technologies, Advanced Robotics and increased Cyber-security for Agile Production in Future European Manufacturing Ecosystems" The TRINITY project has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement No 825196