CREMA | Cloud-based Rapid Elastic MAnufacturing
01-01-2015
-31-12-2017
Projects overview
27 items found
ICP4Life | An Integrated Collaborative Platform for Managing the Product-Service Engineering Lifecycle
01-01-2015
-31-12-2018
BEinCPPS | Business Experiments in Cyber Physical Production Systems
11-01-2015
-31-10-2018
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Technologies and enablers
Information and communication technologies
Result items:
Cloud level: I2Lab
Result items:
The Field/Factory levels feed normalized streams of raw data to the Cloud level for further processing.
Historical analysis of the production process data, through data analysis, will enable to establish trends of the mold usage.
ambliFibre | adaptive model-based Control for laser-assisted Fibre-reinforced tape winding
01-09-2015
-31-08-2018
SAFIRE | Cloud-based Situational Analysis for Factories providing Real-time Reconfiguration Services
01-10-2016
-30-09-2019
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Technologies and enablers
Information and communication technologies
Comment: Methodology for reconfiguration and optimisation of products and factories based on predictive big-data analytics.
Emphasizing security, privacy & trust based on situational awareness.
The methodology will address technical and organizational issues for extension and introduction of tools/services for re-configuration and optimisation of factories / products.
Comment: Predictive Big Data Analytics
TWIN-CONTROL | Twin-model based virtual manufacturing for machine tool-process simulation and control
10-01-2015
-30-09-2018
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Technologies and enablers
Information and communication technologies
FAR-EDGE | Factory Automation Edge Computing Operating System Reference Implementation
01-10-2016
-31-10-2019
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Technologies and enablers
Comment:
The system blueprint is the FAR-EDGE RA. After having defined the requirements and the constraints for each block of the RA, a thorough analysis of the SotA has been done, which led to the identification of some existing software components meeting the specs. We then identified the gaps that the project will need to fill-in: not surprisingly, these where all the key enabling technologies, like the distributed ledger. However, hardly anything is going to be built totally from scratch in FAR-EDGE. The distributed ledger, for example, will be a customization of a generic, open source Blockchain platform (Hyperledger Fabric).
DISRUPT | Decentralised architectures for optimised operations via virtualised processes and manufacturing ecosystem collaboration
01-09-2016
-31-08-2019
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Technologies and enablers
Data collection, storage, analytics, processing and AI
Comment: To deploy analytics both at the lower level, to extract conclusions on machine operations and factory parameters and to detect complex events, and at the higher level to monitor the behaviour of processes and products.
Data analytics and Complex Event Processing (CEP) modules [SAG] mainly at lower levers.
ENCOMPASS | ENgineering COMPASS
01-10-2016
-29-02-2020
vf-OS | Virtual Factory Open Operating System
01-10-2016
-31-10-2019
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Technologies and enablers
Comment:
From a technological point of view, Open vf-OS Platform will provide elements covering the connected world, allowing the exchange and collaboration of information between companies on a value stream thanks to the cloud approach to be adopted (vf-Platform). The Open vf-OS covers from the control device level, where information from the systems (IoT, CPS, embedded systems) is gathered, processed and empowered.
Data collection, storage, analytics, processing and AI
Result items:
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
PreCoM | Predictive Cognitive Maintenance Decision Support System
01-11-2017
-28-02-2021
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Technologies and enablers
Information and communication technologies
SERENA | VerSatilE plug-and-play platform enabling remote pREdictive mainteNAnce
01-10-2017
-31-03-2021
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Technologies and enablers
Information and communication technologies
Data collection, storage, analytics, processing and AI
Comment:
The SERENA porject considers the support of data analytics functionalities for acquiring certainportion of sensor data to feed the machine learning algorithms responsible for predictive maintenance.
DigiPrime | Digital Platform for Circular Economy in Cross-sectorial Sustainable Value Networks
01-01-2020
-31-12-2023
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Technologies and enablers
Information and communication technologies
Data collection, storage, analytics, processing and AI
Result items:
Operational services aim to:
- elaborate and analyse various data collected about post-use batteries to predict the conditions of the battery packs, modules and cells;
- define a Decision Support System to identify the best disassembly and remanufacturing strategy, given the post-use Li-Ion battery conditions.
AI REGIO | Regions and DIHs alliance for AI-driven digital transformation of European Manufacturing SMEs
01-10-2020
-30-09-2023
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Technologies and enablers
Result items:
Result items:
DAT4.ZERO | Data Reliability and Digitally-enhanced Quality Management for Zero Defect Manufacturing in Smart Factories and Ecosystems
01-10-2020
-31-03-2024
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Technologies and enablers
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
Information and communication technologies
Comment: Big data analytics in cloud environments to support manufacturing
scenarios.
Comment: Use of data analytics to provide automated knowledge extraction from big data generated in manufacturing in order to reduce factory and field failures and detect and remedy anomalies earlier.
Result items:
Productive4.0 | Electronics and ICT as enabler for digital industry and optimized supply chain management covering the entire product lifecycle
01-05-2017
-31-10-2020
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Technologies and enablers
Information and communication technologies
Data collection, storage, analytics, processing and AI
Data analytics
OPTIMAL | Automated Maskless Laser Lithography Platform for First Time Right Mixed Scale Patterning
01-10-2022
-30-09-2026
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Technologies and enablers
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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
Information and communication technologies
Comment:
Within the Z-Fact0r, the proposed (higher level) DSS, with the support of the knowledge base and the online inspection module (1st level decision support at single stage), produce, verify and validate decisions aligned with the quality control policies, production targets, desired product specifications and maintenance management requirements. Key functional characteristics of the envisioned DSS incorporates among others, techniques for monitoring and predicting product quality, action prioritization, root cause analysis, and mitigation planning algorithms (at product and workstation level). Moving beyond existing solutions that focus only on specific aspects of the production procedure, or that are restrained to diagnosis, the proposed DSS system incorporates autonomous, hierarchical decision support, based on process analytical technologies and newly developed suitably adjusted knowledge-based systems, and combines product monitoring models and data analytics from heterogeneous sources. The envisioned DSS takes into account a wide set of multiple factors and criteria, such as data uncertainty, lack of information and information quality, involvement of multiple actors, and real-time response. Thanks to the 5 intertwined zero-defect strategies (i.e. Z-PREDICT, Z-PREVENT, Z-DETECT, Z-REPAIR and Z-MANAGE) the overall solution presents a significant contribution to a spectacular improvement in the overall performance and reliability of the targeted multi-stage manufacturing systems and in the production agility (response to continuous adjustments in production targets).
Data analytics
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:
The Z-Break solution uses a variety of communication protocols. HTTP, OPC-UA, IEEE 802.15.4e and IEC WirelessHART. The Hypertext Transfer Protocol (HTTP) is an application protocol for distributed, collaborative, hypermedia information systems. HTTP is the foundation of data communication for the World Wide Web. OPC UA supports two protocols. The binary protocol is opc.tcp://Server and http://Server is for Web Service. Otherwise OPC UA works completely transparent to the API. IEEE 802.15.4 is a technical standard which defines the operation of low-rate wireless personal area networks (LR-WPANs). It specifies the physical layer and media access control for LR-WPANs, and is maintained by the IEEE 802.15 working group, which defined the standard in 2003. WirelessHART is a wireless sensor networking technology based on the Highway Addressable Remote Transducer Protocol (HART). Developed as a multi-vendor, interoperable wireless standard, WirelessHART was defined for the requirements of process field device networks. Also, it uses the NGSI protocol. NGSI is a protocol developed to manage Context Information. It provides operations like managing the context information about context entities, for example the lifetime and quality of information and access (query, subscribe/notify) to the available context Information about context Entities.
Comment:
Z-BRE4K solution provides a big data analytics framework for the identification of the deterioration trends to extended towards prescriptive maintenance. Advanced data analysis tools are under development, to be applied to the quality and production data to realise zero-defect and zero-break down production. Furthermore, it involves models for anomaly detection, that are capable of identifying the machine states where the operation deviated from the norm. This is achieved by collecting the data from the machine sensors in chunks of time and processing them in batch through deep learning models. The models are trying to recreate their inputs, and this results in an observable measure called Reconstruction Error, which is used to identify states that the models aren’t capable of addressing sufficiently (which constitutes an anomaly.
AI-PROFICIENT | Artificial Intelligence for improved PROduction efFICIEncy, quality and maiNTenance
01-11-2020
-31-10-2023
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Technologies and enablers
Information and communication technologies
Data collection, storage, analytics, processing and AI
Comment:
The proposed approach is underpinned by predictive and prescriptive AI analytics at both component and system level, by cross-fertilizing edge and platform AI, while leveraging the human knowledge and feedback for reinforcement learning (human-in-the-loop)
COALA | COgnitive Assisted agile manufacturing for a LAbor force supported by trustworthy Artificial Intelligence
01-10-2020
-30-09-2023
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Technologies and enablers
Result items:
ASSISTANT | leArning and robuSt deciSIon SupporT systems for agile mANufacTuring environments
01-11-2020
-31-10-2023
BOOST 4.0 | Big Data Value Spaces for COmpetitiveness of European COnnected Smart FacTories 4.0
01-01-2018
-31-12-2020
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Technologies and enablers
Information and communication technologies
Data collection, storage, analytics, processing and AI
Data analytics
QU4LITY | Digital Reality in Zero Defect Manufacturing
01-01-2019
-31-07-2022
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Technologies and enablers
Result items:
Result items:
There seems to be relationship to predict torque with use of in-line data. Needs to be more explored
By applying sophisticated algorithms and methods on the acquired data, systematic failure root cause detection supported by data analytics can be implemented. In addition, improved knowledge of machine states/maintenance requirements for neuralgic points can be implemented through the desired solution path within this pilot.
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