MEMAN | Integral Material and Energy flow MANagement in MANufacturing metal mechanic sector
01-01-2015
-30-06-2018
Projects overview
49 items found
MAShES | Multimodal spectrAl control of laSer processing with cognitivE abilities
01-12-2014
-30-11-2017
ambliFibre | adaptive model-based Control for laser-assisted Fibre-reinforced tape winding
01-09-2015
-31-08-2018
ComMUnion | Net-shape joining technology to manufacture 3D multi-materials components based on metal alloys and thermoplastic composites
12-01-2015
-31-05-2019
Eco-Solar | Eco-Solar Factory - 40%plus eco-efficiency gains in the photovoltaic value chain with minimised resource and energy consumption by closed loop systems
10-01-2015
-30-09-2018
COMPOSITION | Ecosystem for Collaborative Manufacturing Processes _ Intra- and Interfactory Integration and Automation
01-09-2016
-31-08-2019
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Technologies and enablers
Mechatronics and robotics technologies
Comment: Continuous monitoring of the condition and performance of specific critical parts (equipment, infrastructure and environment) of the manufacturing system will provide feedback to the real-time brokering and message translations services allowing seamless integration of heterogeneous manufacturing components.
ENCOMPASS | ENgineering COMPASS
01-10-2016
-29-02-2020
KRAKEN | Hybrid automated machine integrating concurrent manufacturing processes, increasing the production volume of functional on-demand using high multi-material deposition rates
01-10-2016
-30-09-2019
ForZDM | Integrated Zero Defect Manufacturing Solution for High Value Adding Multi-stage Manufacturing systems
01-10-2016
-31-03-2021
OpenHybrid | Developing a novel hybrid AM approach which will offer unrivalled flexibility, part quality and productivity
01-10-2016
-30-09-2019
Z-Fact0r | Zero-defect manufacturing strategies towards on-line production management for European factories
01-10-2016
-31-03-2020
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Technologies and enablers
Mechatronics and robotics technologies
Comment:
Z-DETECT is the first strategy of the Z-Fact0r solution: the detection strategy consists of detecting any machining process anomaly or instability through process monitoring by means of controlled variables called critical process variables (CPVs). In particular, this strategy is invoked when a defect is being generated after the adaptation of the parameters. In such a scenario, an alarm is being triggered to flag the parameters that resulted in a defect. By mapping the true reasons, the system will be able to avoid having more generated defects by weighting the system model.
Apart from the inspection of the product from which the defect is being observed, the strategy involves more actions and processes to deal both with the generation of the detected defect, and its propagation to the next stages.
Z-PREDICT strategy is triggered when a defect is recognised during the Z-DETECT stage. The events detected from the physical layer of the system are engineered into high value data that will stipulate new and more accurate process models. Such an unbiased systems behaviour monitoring and analysis provides the basis for enriching the existing knowledge of the system (experience) learning new patterns, raising attention towards behaviour that cause operational and functional discrepancies (e.g. alarms) and the general trends in the shop-floor.
The more the data pool is being increased the more precise (repeatability) and accurate the predictions will be. The estimations for the future states involve the whole production line, e.g. machine status after x number of operations and/or quality of the products for given set of parameters.
The system will predict with high confidence the expected quality and customer satisfaction, allowing modifications to the parameters before the production of the products. In addition, Z-Fact0r can operate in the reverse mode, i.e. insert a Customer Satisfaction Goal and control the parameters accordingly to achieve this target.
The ability of Z-Fact0r to optimise the manufacturing processes according to certain/target quality levels and/or customer satisfaction is the key innovation to fulfil the industrial requirements.
ZAero | Zero-defect manufacturing of composite parts in the aerospace industry
01-10-2016
-30-09-2019
5G-Ensure | 5G Enablers for Network and System Security and Resilience
01-11-2015
-31-10-2017
UPTIME | UNIFIED PREDICTIVE MAINTENANCE SYSTEM
01-09-2017
-28-02-2021
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Technologies and enablers
Mechatronics and robotics technologies
Comment:
UPTIME platform focusses on the use of condition monitoring techniques, e.g. event monitoring and data processing systems, that will enable manufacturing companies having installed sensors to fully exploit the availability of huge amounts of data and to handle the real-time data in complex, dynamics environement in order to get meaningful insights and to decide and act ahead of time to resolve problems before they appear, e.g. to avoid or mitigate the impact of a future failure, in a proactive manner. Moreover, UPTIME proposed unified framework will not be limited to monitoring and diagnosis but it aims to cover the whole prognostic lifecycle from signal processing and diagnostics till prognostics and maintenance decision making along with their interactions with quality management, production planning and logistics decisions.
PreCoM | Predictive Cognitive Maintenance Decision Support System
01-11-2017
-28-02-2021
Z-BRE4K | Strategies and Predictive Maintenance models wrapped around physical systems for Zero-unexpected-Breakdowns and increased operating life of Factories
01-10-2017
-31-03-2021
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Technologies and enablers
Comment:
Z-break will make it possible to combine the current manufacturing systems with current and new mechatronic systems. These combinations will lead to smarter manufacturing systems and thus a shorter ramp up in generating higher quality and productivity.
Comment:
Part of the Z-BRE4K project is the development, of a novel embedded condition monitoring solution with cognitive capabilities, by applying deep learning techniques to reduce the dimensionality of multimodal sensor data associated to a given machine/device, and provide meaningful features to predictive maintenance services on the cloud. Most suitable IoT edge devices, for optimal trade-off between computational power and energy consumption, sensors, providing relevant information of the condition of different components, and signal processing algorithm are proposed for different machines and processes. Data gathering is enabled by the installation of IoT gateways, where data in different protocols are homogenised and sent to the cloud for storage. Real-time data, relevant KPIs and information about components status are visualised through dedicated dashboards.
ROSSINI | RObot enhanced SenSing, INtelligence and actuation to Improve job quality in manufacturing
01-10-2018
-31-03-2022
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Technologies and enablers
Comment:
The ROSSINI modular KIT offers advance components for the different layers of a robotic application (sensing, perception, cognition, control, actuation and integration)
Comment:
The RS4 Controller gather and fuse data from different sensor sources. Within ROSSINI the following sensor sources have been developed as EXTRA components able to be connected with the RS4 controller: 3D Vision cameras, Lidar arrays, Radars and Skins.
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Technologies and enablers
Mechatronics and robotics technologies
Result items:
Nikon K-CMM Metrology - By positioning LEDs on the robot end effector, and on the target parts, the K-CMM system can measure relative positioning to a very high degree of accuracy even over large distances.
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Technologies and enablers
Mechatronics and robotics technologies
Result items:
Nikon K-CMM Metrology - By positioning LEDs on the robot end effector, and on the target parts, the K-CMM system can measure relative positioning to a very high degree of accuracy even over large distances.