X-ACT | Expert cooperative robots for highly skilled operations for the factory of the future
01-10-2012
-30-09-2015
Projects overview
10 items found
SatisFactory | A collaborative and augmented-enabled ecosystem for increasing SATISfaction and working experience in smart FACTORY environments
01-01-2015
-31-12-2017
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Pathways
Comment:
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.
Fortissimo 2 | Factories of the Future Resources, Technology, Infrastructure and Services for Simulation and Modelling 2
11-01-2015
-31-12-2018
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Pathways
Autonomous Smart Factories Pathway
Realtime optimisation
SAFIRE | Cloud-based Situational Analysis for Factories providing Real-time Reconfiguration Services
01-10-2016
-30-09-2019
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Pathways
Autonomous Smart Factories Pathway
Realtime optimisation
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
Realtime optimisation
Result items:
ReconCell | A Reconfigurable robot workCell for fast set-up of automated assembly processes in SMEs
11-01-2015
-28-02-2019
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
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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Pathways
Comment:
Z-Bre4k will provide a complete monitoring solution at component, machine and system level by combining the high capabilities and effectiveness in sensors and actuation, networking and computational power, utilisation of better and smarter technologies (e.g. material and tools). The latest technologies and algorithms will be utilized for adaptive systems while surpassing the fact that they are disjointed, overwhelmed by complexity, vulnerable to external influence and poor Predictive Capabilities.
Comment:
A real-time adaptive simulator with high fidelity will be a demonstrator (remote or local) of the machine’s state, in which a fast-forward simulation mechanism (prognostic models) predicts the potential events of breakdown of components and machines. What-if capabilities will allow the maintenance planners to find the most effective and cost-efficient schedules for component replacement and maintenance plans.
Comment:
Strategies to improve maintainability and increase operating life of production will be applied to update the existing and to develop a set of new strategies based on real data in order to improve maintainability and operating life of production systems. This approach will use a method to translate optimization objectives defined at production and factory levels, into optimized maintenance policies at asset/production process levels.
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:
Solutions, methodologies, and tools that are being developed within different work packages of QU4LITY are being applied to this pilot in the task T7.2 of WP7. As is shown in the figure, different components of the FAGOR platform such as FA-LINK, IKCLOUD+ and IKSEC+ are being extended focusing in ZDM of press machines. FA-LINK platform has been completed with the following components:
- ETL@FA-LINK is responsible to extract transform and load the data. Additionally to the sensor data, and with ZDM on mind, contextual data is also collected by the platform. This component prepares the data for the AI training process. This component is composed by an extended version of iKCloud that is being designed in WP3 and integrated in this pilot in WP7.
- Then, TRAIN@FA-LINK uses the previously obtained and persisted data to create an AI model for ZDM. The model is generated using cutting edge machine learning technologies. This model is used to identify situations and generate suggestions to the users to increase performance and reduce defectives of press machine. The model training is performed by the extension of ikCloud+ developed in WP3/WP7 work packages of QU4LITY.
- Next, the model is executed by EXECUTE@FA-LINK using the data obtained from the press machine. This way, ZDM related alarms, indicators and suggestions are obtained and persisted. This component uses FA-LINK and IK-Cloud+ solutions that are being implemented in WP3 and WP7.
- Finally, the obtained results are shown to the users using different views of FA-LINK. A set of new views focused on ZDM and quality are being implemented in the WP7 of QU4LITY in this pilot. These new views provide the ability to the platform to show, suggest and guide the user to improve press machine performance and reduce downtimes and defectives.
The variables monitored in real time throght IoT platfroms for 2 manufacturing lines enables the Zero Defect Manufuactiring goal. Multiple industrial assets monitored force to have an strategic in terms of enchacement processes.
The implementation of modular architecture interconected involving Cloud and Edge Systems, Data Modelling and Learning Service and Iot Hub produce top quality production. The introduction of Interoperability layer for gathering data from two different manufacturing lines together with OPC-UA and AAS is key for the goal.
The ambition is to create a modular monitoring and control system that can be used with many different sensors and process models. The models need to be adaptable to the actual task, for a specific geometry or dedicated material processing conditions. Real-time process and machine signals need to be analysed in by machine-learning algorithms to find structures and pattern related to the required key quality indicators (critical defects per track, distortion, keeping of dimensions).The system will be also connected to a higher-level factory data interface which allows to exchange process information and reassign the production strategy based on additional factory conditions.
In the RiaStone Qu4lity Pilot the ZDM-AQL is implemented in a modular architecture, which includes both in-factory data processing, Edge processing Systems, Cloud processing systems, and Machine Learning processing Services.
The RiaStone Qu4lity Pilot goal is to recognize, detect, and reconfigure the production process parameters as soon as a failure is detected in real-time.
This process is based in the collected data, advanced analytics, machine learning image inspection methods
Result items:
Process Data from Manufacturing Data Lake is analysed online for suspicious outliers. A Webinterface enables online intervention by staff.
MONDRAGON pilot is being developed considering 2 IoT platfrom and interoperability layer developed by MGEP together with OPC-UA and AAS. Real time process optimisation enables Autonomous quality outcomes and Zero Defect Manufacturing for Automotve (Fagor Arrasate )and railway (Danobat) manufacturing lines. The FA-LINK platfrom monitored industrial assets for Fagor Arrasate and SAVVY IoT platfrom for DANOBAT.
The introduction of IA algorithms by ATLANTIS and VTT are developed offline achieving high top optimisation production. On the other hand, the approach of Machine Learning approach should be further developed. The interaction of the operators, maintenance workers and R&D staff are stil crucial for Top high level Autonomous Manufacturing process Optmisation
Part of the improved decision process enabled by the holistic platrom can be close looped into machine control parametes, allowing an autonomous quality management at factory level
Integration of Robot and human processes at station level.