MUlti-layers control&cognitive System to drive metal and plastic production line for Injected Components
High Pressure Die Casting (HPDC) of light alloys and Plastic Injection Molding (PIM) are two of the most representative large-scale production-line in manufacturing field, which are strategic for the EU-industry largely dominated by SMEs. Due to the high number of process variables involved and to the non-sinchronisation of the process control units, HPDC and PIM are most "defect-generating" and "energy-consumption" processes in EU industry. In both, sustainability issue imposes that machines/systems are able to efficiently and ecologically support the production with higher quality, faster delivery times, and shorter times between successive generations of products.
The MUSIC project 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 tool, based on innovative Control and Cognitive system linked to real time monitoring, that allow an active control of quality, avoiding the presence of defects or over-cost by directly acting on the process-machine variables optimization or equipment boundary conditions.
The Intelligent Manufacturing approach will work at machine-mold project level to optimise/adapt the production of the specific product and can be extended at factory level to select/plan the appropriated production line. The sensors calibration and quality control of measurements will be the pre-requisite of Intelligent Sensor Network to monitor the real-time production and specific focus will be also devoted to Standardization issues.
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.
| Web resources: |
http://music.eucoord.com/
https://cordis.europa.eu/project/rcn//factsheet/en |
| Start date: | 01-09-2012 |
| End date: | 31-08-2016 |
| Total budget - Public funding: | 9 302 073,00 Euro - 6 135 000,00 Euro |
| Call topic: | Smart Factories: Energy-aware, agile manufacturing and customisation (FoF.ICT.2011.7.1) |
This is a set of Specific Objectives and Research & Innovation Objectives that is subject to a consultation in preparation of the Made In Europe Partnership. For more guidance about the consultation, please see www.effra.eu/made-in-europe-state-play.
The MUSIC project results are in agreement to Smart Manufacturing strategy that is oriented to zero defect and system efficiency with the support of new ICT platform with full interoperability of systems, with flexibility for small or large factory, with optimal solution for one size or serial production, improving the human machine interaction as well as sharing the information along the supply chain
The Remote control of multi-stages production chain montitors the process stability, the stops and times. The monitoring is applicable to existing or new production lines independently by the trademark. The Smart Monitoring module is collecting all process data in a server database (or cloud), via OPC_UA protocol, coming from all existing devices and active sensors in the production line.
The platform is scalable and flexible enablign the comntrol of multi-lines factory wiht different sites, as well as it can open the collaboration along the supply chain.
It increases the knowledge of the process from the data and re-use the best practice for next batch or similar components. The real-time visualization of elaborated data, including warning and alarm messages and statistic production diagrams, can be customized for multiple users’ interfaces as machine operator, production manager and plant director.
The Real Time improvement of Overall Equipment Effectiveness (OEE) takes into account the Availability (Unplanned and Planned Stops), the Performance (FastCycles and Small Stops) and Quality (Good parts produced). The KPIs of the OEE are immediately elaborated monitoring the time of each stage in the cycle and predicting the defects by implemented cognitive model.
The application of Artificial Intelligence, the cognitive approach, supports the process optimization with proper suggested reactions. The smart web Graphical User Interface (GUI) visualizes the data and deviation, share and communicate the significant KPI to support the decision making with proper reactions in real-time.
The intelligent process data management, bases on Data-Driven Digital Twin, is applicable to traditional or advanced manufacturing processes (e.g. casting, thermoforming, etc or the innovative additive manufacturing) always orienetd to ZDM for smart and complex products.
The data is the fuel of Data-driven digitla twin. The innovation is the combining of real data and virtual data coming from simulation of the manufactruing process.
The data-driven digital twin in production line is a solution to measure, analyze and react oriented to zero defect manufacturing and to the maximization of the OEE improving the sustainability and profit of factory of future.
The application of Smart Prod ACTIVE system has been demonstrated and validated at foundry level. In the frame of HPDC production process, Operator and Process manager take advantage by adopting a centralized remote control system supporting process monitoring and quality prediction in real time. The decision is supported by cause-effect correlations, and proper reactions suggested by a continuously updated meta-model. Re-usability and flexibility of the Smart Prod ACTIVE system also allow agile re-start in case of small batches production
The first successuful testing and applications are referred to SMEs
The same aproach is valid of any multi-stages production chain to produce parrt for all industrial sectors.
The challenge is the sensorization and data acquisition in the existing equipment not properly designed.
The data miming, real-time alerting and cognitive predictive meta-modelling is not a chimera.
A fundamental innovative characteristic of digital twin platform, called Smart Prod ACTIVE, is the predictive Quality model integrating multi-resolution and multi-variate process data in a collaborative way to support the decision making process by Operator (Quality oriented), Production manager (Efficiency oriented - OEE) and Business manager (Cost-oriented)
The equipment and devices are sensorized to be deeper monitored
Manufacturing cell is monitored with data acquisition from any level.
All stages of production line are connecteted to the Data-Driven Digital Twin platform
All different production lines and plants can be monitored wiht the same platform because it is felxible and scalable.
Data-Driven Digital Twin enables the collaboration between OEM and Suppliers
web-based platform and the access management is assential to collaborative platform sharing production data
Next future development and application is the automatic and intelligent retrofit excluding the current communication limits.
The data-driven digital twin is between SCADA and MOM
The data-driven digital twin is between SCADA and MOM
The GUI open the interaction with data-driven digital twin to data entry and KPI analytics
The data-driven digital twin enables the real-time process optimization contorlling the process deviation affceting the quality and efficiency
The data-driven digital twin uses CAD model of product and equipment
OPC_UA has been intensively applied in the Data_driven Digital Twin of existing production line
OPC_UA has been intensively applied in the Data_driven Digital Twin of existing production line
Result of the MUSIC project is a new starndard of Aluminium and aluminium alloys — Classification of Defects and Imperfections in High Pressure, Low Pressure and Gravity Die Cast Products, CEN, Brussels (2014).
CEN T/R 16749: Aluminium and aluminium alloys — Classification of Defects and Imperfections in High Pressure, Low Pressure and Gravity Die Cast Products, CEN, Brussels (2014).
The Data-Driven Digita Twin Platform, Smart ProdACTIVE , is property of EnginSoft
-
New Total Thermal Vision System ...
- Monitoring System HW and SW
- Soundsensor
- Apparatus for measuring a vacuum in the cavit...
- Apparatus for improving the quality of die-ca...
- Cost & Energy models
-
New Control & Cognitive System ...
-
Device monitoring the production of pressure ...
-
Advanced Temperature Control Unit ...
-
Self-Adaptive Chill-Vent ...
-
Smart Control and Cognitive System applied to...
-
The MUSIC guide to key-parameters in High Pre...
- Product counter ...
-
| EnginSoft SpA (Coördinator)
-
| ASSOMET SERVIZI S.R.L.
-
| Audi AG
-
| ELECTRONICS GMBH VERTRIEB ELEKTRONISCHER GERATE
-
| Eurecat
-
| Fraunhofer IFAM
-
| HOCHSCHULE AALEN - TECHNIK UND WIRTSCHAFT
-
| IK4-Tekniker
-
| MAGMA GIESSEREITECHNOLOGIE GMBH
-
| MAIER, S.Coop.
-
| MOTUL SA
-
| OSKAR FRECH GMBH CO KG
-
| RDS MOULDING TECHNOLOGY SPA
-
| REGLOPLAS AG
-
| TOOLCAST SNC DI VOLTAZZA SAMUELA &C.