MUSIC
MUlti-layers control&cognitive System to drive metal and plastic production line for Injected Components
Manufacturing/ICT
Control and Cognitive system to drive Injected Components production lines
Due to the high number of process variables involved and to the non-synchronisation of the process control units, High Pressure Die Casting (HPDC) of light alloys and Plastic Injection Moulding (PIM) are most “defect-generating” and “energy-consumption” processes in EU industry showing less flexibility to any changes in products and in process evolution. Therefore, the MUSIC is strongly aimed at leading EU-HPDC/PIM factories to cost-based competitive advantage through the necessary transition to a demand-driven industry with lower waste generation, efficiency, robustness and minimum energy consumption. The development and integration of a completely new ICT platform, based on innovative Control and Cognitive system linked to real time monitoring, allows an active control of quality, avoiding the presence of defects or over-cost by directly acting on the process-machine variables optimisation or equipment boundary conditions.
The challenge of MUSIC is to transform a production-rate-dominated manufacturing field into a quality/efficiency-driven and integration-oriented one to exploit the enormous (and still underestimated) potential of HPDC/PIM through collaborative research and technological development, along the value chain through advances in manufacturing, ICT and model process technologies.
Exploitation of data acquisition, data elaboration and process modeling capabilities for the implementation of a commercial software for future distribution within manufacturing sectors.
EnginSoft SpA | Electronics GmbH | University of Aalen – GTA | MAGMA GmbH | University of Padova – DTG | Fundacion Tekniker | Eurecat - Technology Centre of Catalonia | Oskar Frech GmbH + Co. KG | Saen | Maier S.Coop | Audi AG | RDS Moulding Technology | Motul | Regloplas AG | Fraunhofer-Institute IFAM | Assomet Servizi
Funding Scheme FP7 Collaborative Project | Call identifier FP7-2012-NMP-FoF-ICT
48 months
September 2012 - August 2016
EnginSoft Spa
Nicola Gramegna
16
Some of our competences in research and technology transfer
Research project
Attraverso un innovativo approccio Digital Twin in ambito Prognostics Health Monitoring (PHM) è possibile l’individuazione di comportamenti anomali dei sistemi monitorati, che potrebbero portare a rotture impreviste durante il servizio, aumentando la capacità di diagnostica di guasto. Il monitoraggio consente una migliore pianificazione degli interventi manutentivi potendo valutare costi dell’intervento ed efficienza residua del sistema.
Research project
YOGA develops a semiautomated and standardized platform targeting clinical laboratories for tissue characterization by biopsy when very little material is available.
