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Develop the laser-cladding remanufacturing path planning from the 3D scan of the real crankshaft component by creating the trajectories of robot system and automate the laser coating processes for each type of crankshaft reference.
For early fault detection, a Graph Deviation Networks (GDNs)-based approach can be used to analyze the data obtained from battery cells to create a graphical representation of the battery system as a network. By analyzing the data and the structure of the network, GDNs can detect deviations or anomalies from the expected behavior, which indicate the presence of faults or damage in the cells. The earlier misbehavior in EV batteries can be detected, the more likely a dedicated repair process (remanufacturing) can be designed and implemented.
Remanufacturing or reuse of wind turbine gearbox components has so far been very limited. With R3-MYDAS, additive manufacturing will be a new practice and big improvement for reuse of failed components. The replacement of the failed component with updated technology that mitigates specific problems will have an important role in minimizing future failure rate.