Project: QU4LITY
Type: | Updated at: 31-05-2021
Associated projects
Autonomous Smart Factories Pathway
Specifics for Result PRIMA_PILOT | PRIMA Additive Manufacturing Pilot Adaptive Control Technology
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Digitalisation pathways
Demonstrator specific Kanban
Digitalisation pathways
Autonomous Smart Factories Pathway
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Demonstrator specific Kanban
Comment:
The POWDER BED Additive technology will be considered to test new edge devices for process control, towards a ZDM result, and to work on data management and analytics to implement the whole manufacturing process by a platform approach. Data monitored from the machine tool and meta-information generated by different applications running at edge level will be collected and elaborated by the data analysis tool to extract useful information to be sent to the decision support system.
General purpose software
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Demonstrator specific Kanban
Comment:
Main technologies that will be adopted in this pilot:
Realtime optimisation
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Demonstrator specific Kanban
Comment:
The ambition is to create a modular monitoring and control system that can be used with many different sensors and process models. The models need to be adaptable to the actual task, for a specific geometry or dedicated material processing conditions. Real-time process and machine signals need to be analysed in by machine-learning algorithms to find structures and pattern related to the required key quality indicators (critical defects per track, distortion, keeping of dimensions).The system will be also connected to a higher-level factory data interface which allows to exchange process information and reassign the production strategy based on additional factory conditions.
Autonomous /online/realtime Manufacturing Process Optimisation on machine level
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Comment:
Thanks to this new approach with modular adaptable signal processing system and a strong interaction with data space and simulation tools trough the platform, will be possible to detect anomaly and have anequipment condition reporting , reduce reject rate by application of data-driven process model that has been derived by AI algorithms, increase OEE by recommending process adjustments to the operator or directly change the parameters in real time, so to reduce also the operator costs. |