MERGING | Manipulation Enhancement through Robotic Guidance and Intelligent Novel Grippers
01-11-2019
-31-10-2023
01-11-2019
-31-10-2023
01-09-2022
-31-08-2025
01-11-2018
-30-04-2022
Used components:
IDS connectors for data provider, data consumer ; Clearing house ; Broker
01-11-2019
-30-04-2023
01-10-2019
-30-09-2023
01-01-2020
-31-12-2023
Interoperability (sharing of data and resources between different systems)
01-05-2019
-31-07-2022
Check video at 48:05 and 1:51:04 (translation system)
01-01-2019
-30-06-2023
01-01-2019
-31-12-2022
01-01-2019
-31-07-2022
Associated to QU4LITY Reference Architecture: Corporate Network/ Production OT Access Network: Deterministic Ethernet (TSN), OPC-UA, IDS/NGSI-LD
Details: OPC UA: an industrial M2M communication protocol for Interoperability; Information modelling
Associated to QU4LITY Reference Architecture: Corporate Network/ Production OT Access Network: Deterministic Ethernet (TSN), OPC-UA, IDS/NGSI-LD
Details: OPC UA: an industrial M2M communication protocol for Interoperability; Information modelling
Associated to QU4LITY Reference Architecture: Corporate Network/ Production OT Access Network: Deterministic Ethernet (TSN), OPC-UA, IDS/NGSI-LD
Details: OPC UA: an industrial M2M communication protocol for Interoperability; Information modelling
There was a risk that other developments made within this pilot do not follow the reference architecture of IDS and thus are incompatible. This would cause that certain applications could not be deployed and run within in the proposed data space approach.
01-10-2018
-31-03-2022
The ROSSINI Controller (Semantic Scena Map, Flexible and Execution layers) is designed to improved efficiency in the production line/robotic cell (adapting to the possible changing in the environment and including job quality factors)
The RS4 System (RS4 Controller and sensors) is designed to improve safety also for standard robots
The ROSSINI Modular KIT is a set of components that can be integrated to implement robotic workcells, capable of increasing job quality and reducing reconfiguration time.
01-10-2017
-31-03-2021
Α Web API will return semantic data. The communication interface is through the SPARQL query engine. Z-BRE4K ontology is implemented with the Open Semantic Framework (OSF), an integrated software stack using semantic technologies for knowledge management. Furthermore, JSON formatted data from the shop floor is transferred through a MQTT broker, to be finally stored in I-LiKe machines internal data repository. IDS connectors are used to transform data into the NGSI format, move the data to the ORION context broker to be finally consumed by other applications. Also, the Quality Information Framework (QIF) standard guarantees interoperability since it defines an integrated set of information models that enable the effective exchange of metrology data throughout the entire manufacturing quality measurement process – from product design to inspection planning to execution to analysis and reporting. OpenCPPS (part of AUTOWARE) will provide support for selected mainstream communication protocols and will define the proper interfaces for other communication protocols to be plugged-in.
The Z-Bre4k solution is based on the blackboard architectural model. This model is mainly an artificial intelligence approach, where a common knowledge base, the "blackboard", is iteratively updated by a diverse group of specialist knowledge sources, starting with a problem specification and ending with a solution. Each knowledge source updates the blackboard with a partial solution when its internal constraints match the blackboard state. In this way, the specialists work together to solve the problem. The blackboard model was originally designed as a way to handle complex, ill-defined problems, where the solution is the sum of its parts. The blackboard component acts as a central repository system. The rest of the software applications (components) act independently at the common data structure stored on the blackboard, they respond on changes and create new reactions according to changes. Interaction between components is implemented via the blackboard.
01-09-2017
-31-08-2020
The UPTIME conceptual architecture was designed according to the ISO/IEC/IEEE 42010 “System and software engineering – Architecture description” and mapped to RAMI 4.0 in order to ensure that it can be represent predictive maintenance in the frame of Industry 4.0.
The UPTIME vision converges and synthesizes predictive maintenance, proactive computing, the Gartner’s levels of industrial analytics maturity and the ISO 13374 as implemented in MIMOSA OSA-CBM in order to create a consistent basis for a generic predictive maintenance architecture in an IoT-based industrial environment. In this way, the Operational Technology and the Information Technology can also be converged in the context of Industry 4.0.
01-11-2017
-31-10-2020
01-06-2017
-31-05-2019
01-10-2022
-30-09-2026
02-09-2013
-01-09-2017
01-09-2016
-31-08-2019
01-12-2019
-31-05-2022
01-10-2012
-30-09-2015
01-10-2016
-31-03-2020
Data communication between components is essential for the project. End users create data on their shop floor with embedded sensors on the machines, new integrated sensors developed for the project. All these data is propagated in the system with data communication protocols, such as HTTP and AMQP, creating a data stream process in the system. Interoperability between the data communication protocolos and brockers is crucial for a successful result of the data communication of the system. Various data sources work together and use different communication protocols. As a result, all these components and protocols should seamlessly work and their interoperability is what helps them. A message brocker was developed for the project, based on AMQP for data communication. In the initial phases of the project, there were also RESTful APIs that helped in the initial development of the components.
Z-Fact0r hybrid framework, obtained by applying a software and hardware integration strategy, is installed on the industrial end users shop floors. This architecture exploits features from Relational Databases and Triplestore while using the blackboard architectural pattern which ensures efficient and accurate communication of data transfer among software applications and devices.
01-01-2015
-01-01-2018
01-10-2016
-30-09-2019
01-09-2016
-31-08-2019
01-10-2016
-30-10-2019
01-09-2012
-31-08-2016
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
01-10-2016
-30-09-2019
01-10-2016
-30-09-2019
M4.0 metal domain addresses the BUSINESS LAYER of the vertical side of RAMI and the ENTERPRISE and CONNECTED WORLD layers of the hyerarchical side