SatisFactory | A collaborative and augmented-enabled ecosystem for increasing SATISfaction and working experience in smart FACTORY environments
01-01-2015
-31-12-2017
Projects overview
13 items found
AUTOWARE | Wireless Autonomous, Reliable and Resilient ProductIon Operation ARchitecture for Cognitive Manufacturing
01-10-2016
-30-09-2019
TWIN-CONTROL | Twin-model based virtual manufacturing for machine tool-process simulation and control
10-01-2015
-30-09-2018
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Pathways
Autonomous Smart Factories Pathway
Off-line optimisation
Result items:
COMPOSITION | Ecosystem for Collaborative Manufacturing Processes _ Intra- and Interfactory Integration and Automation
01-09-2016
-31-08-2019
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Pathways
Comment:
The concept of the autonomous factories is approached in the intrafactory part of the project with connections between different links of the value chain. Agent marketplace and automated bidding process which enable automated negotiation and transaction.
FAR-EDGE | Factory Automation Edge Computing Operating System Reference Implementation
01-10-2016
-31-10-2019
Daedalus | Distributed control and simulAtion platform to support an Ecosystem of DigitAL aUtomation developerS
01-10-2016
-30-09-2019
DISRUPT | Decentralised architectures for optimised operations via virtualised processes and manufacturing ecosystem collaboration
01-09-2016
-31-08-2019
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Pathways
Autonomous Smart Factories Pathway
Off-line optimisation
Result items:
UPTIME | UNIFIED PREDICTIVE MAINTENANCE SYSTEM
01-09-2017
-28-02-2021
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Pathways
Comment:
UPTIME will provide a unified predictive maintenance management framework and a smart predictive maintenance information system covering the whole prognostic lifecycle. It will contribute to improve smart predictive maintenance systems capable to integrate information from many different sources and of various types, in order to more accurately estimate the process performances and the remaining useful life.
SERENA | VerSatilE plug-and-play platform enabling remote pREdictive mainteNAnce
01-10-2017
-31-03-2021
QU4LITY | Digital Reality in Zero Defect Manufacturing
01-01-2019
-31-07-2022
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Result items:
The objective of the pilot is to enable smart machines with autonomous diagnosis based on machine condition monitoring.
FAGOR ARRASATE as a leading manufacturer of forming machines it is obliged to proactive participate in projects like QU4LITY and led solutions to the customers to improve the availability, performance and quality of their installations and get an optimum cost per part ratio.
FAGOR ARRASATE has a long experience in delivering press machines as well as providing the building blocks of such lines. A press machine is the product par excellence of FAGOR ARRASATE. A typical press machine is composed by two rigid platforms (head and base), a bed, a ram, and a mechanism as well as all the other surrounding components that guarantee the full automation and process control.
Historically, machine tool manufacturers have not had any information of the machine behaviour once they were working at the customer facilities. Maintenance actions by the machine tool supplier, where mainly started by a customer’s call and where mainly related to corrective actions, once the failure had already happened.
Currently many condition issues on the machine are detected afterwards, they appear when a quality matter is detected on the forming parts or a machine component is damaged, causing even machine stoppage. These problems are fixed by machine adjustment or changing programs or forming process parameters.
Consequently, the only way to avoid future problems is by preventive maintenance or machine adjustment actions. These are carried out either by the machine owner itself or external services which are sometimes delivered by FAGOR ARRASATE.
In QUALITY project, FAGOR ARRASATE will equip a press machine with a SMART CONNECT technology that provides data from the machine, to the owner and to the machine supplier. Within the context of Zero-Defect Manufacturing, FAGOR ARRASATE will develops Smart solutions that will anticipate and avoid failures, reduce downtimes and assure quality.
It has a great complexity from the point of view of the acquisition, measurement and transmission of the parameters and variables. The result that would be obtained from the QU4LITY project, would allow the customers of FAGOR ARRASATE to have total control of a zero defects manufacturing process at machine level and to know at any time how and under which conditions all the parts have been manufactured.
Within Qu4lity use case, GHI with the collaboration of Innovalia and SQS, is building a ZDM scenario based on the development of a smart and connected hot stamping process with the ability to correlate the furnace operation parameters with the quality control of the stamped parts, extending in this way the product lifecycle control loop, making the operator more involved in the process thanks to the new platform developed.
Kolektor's Qu4lity project is addressing the real-time injection moulding process monitoring-control. The scope of the pilot project is a production line where Kolektor produces one type of product. The aim of this pilot is to detect, possibly predict, and remove the cause of the process failure as soon as possible, ideally in real-time. Based on the collected data and by applying the control loops, advanced analytics, and artificial intelligence methods we are trying to better understand the moulding process, with the emphasis on detecting anomalies and failures as soon as possible.
The POWDER BED Additive technology will be considered to test new edge devices for process control, towards a ZDM result, and to work on data management and analytics to implement the whole manufacturing process by a platform approach.
Data monitored from the machine tool and meta-information generated by different applications running at edge level will be collected and elaborated by the data analysis tool to extract useful information to be sent to the decision support system.
Using the opportunities brought by the Qu4lity project, RiaStone with the collaboration of Synesis and IntraSoft, built a commercial grade ZDM implementation scenario, which brings to the ceramics industry the ability to implement Autonomous Quality Loops, which will add new approaches to production, promoting better and innovative defect management and production control methods, consistent with the integration of Zero defect Manufacturing processes, these being namely: in-line inspection technologies, and integration of ICT tools for autonomous, automatic, smart system decision taking
The production line in Amberg has a highly automated process with several test stations along the path.
Result items:
The gathering of data from the machines during their lifetime allows the generation of valuable information for the improvement, not only of the actual machine and process, but also the future machines and smart functions that are continously improved through data based engineering and design.
FAGOR FA-Link MAP (Fagor Arrasate Link MAchine Platform) is a platform developed by FAGOR ARRASATE in collaboration with IKERLAN. This platform uses cutting edge technologies for big data processing and visualization. This platform inputs the data from the press machine using the FAGOR (Data-Adquisition System) and provides to the different stakeholders an UI with a set of views to monitor and analyze the press machine performance. The UI is customizable by the end user and the data to be shown is manually configured as different views and alarms in FA-LINK visualization UI.
FAGOR’s monitoring platform, FA-Link machine platform, is being extended with tools focused on ZDM in this pilot and in the related packages of QU4LITY.
FA-Link is composed by two systems, the former that is executed in the manufacturing plant (on premise) and the latter that is executed in the cloud. The data captured in plant via on premise (local view), is uploaded and aggregated in the cloud part (global view).
The Amberg production line collects all the process and test data an a quality management system data base. This allows reporting and production KPI analysis as well as supply chain management.
Result items:
FAGOR’s monitoring platform, FA-Link machine platform, is being extended with tools focused on ZDM in this pilot and in the related packages of QU4LITY.
There are two IoT platforms included in 2 manufacturaing lines for Automotive and Railway sector for MONDRAGON pilot. IoT platforms monitor 3 grinding machines from railway sector as well as press machine, stacker, Owen and Transfer from Automiotive customer.
The variables monitored allow machine tool buider to know that the process exectuion is under treshold defined as well as enabling predictions about possible faillures. As a consequence there will be an increase of the quality production and the optimisation of the production.
The interoperability layer between two IoT Platfroms has been achieved considering OPC-UA and AAS.
Collaboration with international partners such as ATLANTIS, VTT and FHG has allowed us to include IA algorithms, specific data analitics and data sharing connectors.In spte of the fact that IoT platfrom and interopoerability has been oriented to real time optimisation the analitycs and the IA algorithms have been carryed out off-line
With the quality management platform, optimization of production is enabled.
The optimization of quality process decision is taking place thank to a holistic view of the factors that influence the perception of the quality from the consumer prespective. The platform using a MPFQ driven data model is enabling a faster, more reliable and flexible visualization system and analytical approach.
ZDMP | Zero Defect Manufacturing Platform
01-01-2019
-30-06-2023
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Result items:
Result items:
The ZDMP platform provides an optimisation services for the quality check performed within the CONT assembly line, resulting in the reduction of false-positives during the automatic test, as well as creating the models based on operator decisions used for generation of acceptance patterns.
In this use-case ZDMP platform provides a middleware between the workstations and the database keeping the production process details. Moreover, it provides a set of services for the production process improvement.
ZDMP platform allows automated exchange of the critical information that can affect the schedule. Ability to adjust the activities regarding potential delays has significant impact on the construction consortia performance.
The ZDMP platform assists the process of quality inspection, while providing a library inspection programs for specific materials/components. Moreover, to understand the tendency over time current measurements can be compared with historical ones.
The process data required for anomaly detection and production process optimization are gathered from multiply sources both in MRHS and in FORD and aggregated to predict the quality of the product and a rejection probability. Based on the data gathered, the model for parameters’ optimization is generated to achieve a certain, user-defined, objective.
The ZDMP platform offers for both industrial partners an opportunity to improve the communication, through knowledge generation from the raw data. The platform also offers a service for the equipment optimisation to improve the production process.
The ZDMP platform which is deployed outside of the FORM facility, allows for FORM to reduce the maintenance and investment costs for an internal platform that is important for SME. Moreover, ZDMP platform, as data and knowledge aggregator can be utilized by all industrial partners in order to optimise the production process.
The ZDMP platform which is deployed outside of FORM, allows for FORM to simplify the process for data acquisition and processing enabling quick and effortless way of anti-collision system utilization.
In this use-case significant impact is made by the tools provided by ZDMP platform addressing the automation of the quality control process reducing the load on operators.
Utilization of ZDMP platform with corresponding services allows optimization of production process in terms of automation of production and natural defects and better use material through spatial mould optimization.
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Pathways
Autonomous Smart Factories Pathway
Result items:
Result items:
Siemens Mindsphere will be used to collect and analyse data from the shop floor; cloud-based.
Amazon Web Services – Currently used to host cloud data and machine learning algorithms.
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Pathways
Autonomous Smart Factories Pathway
Result items:
Result items:
Siemens Mindsphere will be used to collect and analyse data from the shop floor; cloud-based.
Amazon Web Services – Currently used to host cloud data and machine learning algorithms.
Autonomy in factories is achieved by security systems that produce alerts and warnings, by training courses that does not require a trainer and by applications that signs daily jobs automatically to the most appropriate employees based on specific criteria.