The FRAME project aims to reduce time-to-market and time-to-volume for new and reconfigured systems by more than 30% through steeper learning curves of self aware and more adaptive machines.
Similarly, time-to-recover from unpredictable events will be halved compared to the current-state-of- the-art using the knowledge a system has accumulated over its operational life. The development of a holistic, continuously learning system operation and optimisation environment is expected to raise the overall achievable performance and availability of assembly systems. Overall this is expected to result into significantly more efficient manufacturing systems which will gain increased competitive advantage of European manufacturers by 20-25% percent in the medium to long term.
Reduced time for system reconfiguration by 30%
Reduced ramp-up times by 30%
Back tracing relevant data of all processes within the system and respective products leading to improved process validation & quality
Increased system flexibility leading to increased level of product customisation by 40%
Downtimes reduced by 30%
Reduction of ramp-up delay factors by 50%
System monitoring, optimisation and verification leading to reduction of rework by 30%
Historical data shows that on average 60% of the total time taken in ramp-up of assembly systems is spent in error identification, location and recovery. The initial integration and rectification actions require only around 40% of the time. Furthermore, the ramp-up effort contributes to around 65% of the cost of the system. Therefore, by introducing systems that can recognise and identify faults and learn the best solutions to bring a system up to and beyond full volume production, the ramp-up time can be significantly reduced. Indeed, by offering capabilities which reduce fault detection times by 50% will result in the total ramp-up time being reduced by 30%.