The sustainable refabrication of hardware components usually requires a large amount of manual labor. The wear and tear of used components impacts obejct geometries and surface characteristics in unique ways which are difficult to predict. This renders automation difficult and makes refabrication – despite being ecologically superior – less economically viable. RoboGrind unites ecological and economic sustainability via the use of artificial intelligence. Machine learning techniques permit an automated, fully individualized treatment of single workpieces, which increases the economic viability of refabrication. The AI-based surface treatment system will combine sensor-based object scanning and visual inspection with industrial robot actuators and matching surface treatment tools, as well as an intuitive, human-centric user interface. To achieve high precision and near-autonomous planning and execution, task planning and force control are augmented by machine learning techniques. The system will be tested in the context of refabrication in the green mobility, energy storage and energy generation sectors.