The SeConRob project aims at developing methods for the self-configuration of robotic processes, where each manufacturing step depends on the results of the previous step. In this case a lot of productivity, energy and resources are lost, because the processes currently cannot be automated for technical and economic reasons. Such situations typically occur during inspection and re-work, where the (downstream) re-work process depends on the results of the (upstream) inspection process of each individual part. SeConRob will develop technologies that enable the automation of such processes, by creating robotic processes that can be automatically configured for each individual part. This will build upon AI-based data analysis that extracts information from the inspection data, that used in turn to automatically generate a robot program and process parameters for the downstream re-work process. Physical process models will the basis for the initial planning and a long-term feedback loop based on reinforcement learning will be established to optimize the process and account for properties that are not included in the initial model.
Two use cases with multi-stage manufacturing processes including inspection, gouging, welding, grinding and polishing will provide test cases for the developments. Demonstrations are planned on a real-world production line to raise interest in sectors such as automotive and aerospace, where safety-critical parts are manufactured. The estimated market potential for such multi-stage self-configuring robotic process is about 2000 robotic workcells, corresponding to a market of 600 M€.