In automated production processes, unplanned stops occur due to tool failure, material defects, unmanaged program situations or human interventions, etc. Restart of production defined as an error recovery process is a complex problem, involving the three phases: error detection, error diagnoses, and resetting the system to a restartable state. Short, long and unplanned production stops are expensive, unavoidable and cannot be predicted and therefore cannot be handled in advance in system and process models. There is no one-size-fits-all solution, rather each situation must be handled by the operator or maintenance personnel.
The envisioned system prototype is expected to facilitate in particular the error diagnosis aspect of the recovery process through hands-on AR guidance tuned to the specific situation and skill level of the operator. The guidance will be based on error cause diagnosis strategies prerecorded by expert maintenance personnel, strategies which the system automatically links to the current unexpected shop floor situation through AI. For recording and representing these strategies, various technologies will be investigated including state-of-the art AI activity sequencing approaches, eye tracking, and AR-based subtle guidance approaches.