MUSIC | MUlti-layers control&cognitive System to drive metal and plastic production line for Injected Components
01-09-2012
-31-08-2016
Projects overview
12 items found
X-ACT | Expert cooperative robots for highly skilled operations for the factory of the future
01-10-2012
-30-09-2015
PREVIEW | PREdictiVe system to recommend Injection mold sEtup in Wireless sensor networks
01-01-2015
-31-12-2017
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Pathways
Autonomous Smart Factories Pathway
Realtime optimisation
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.
SYMBIO-TIC | Symbiotic Human-Robot Collaborative Assembly: Technologies, Innovations and Competitiveness
01-04-2015
-31-03-2019
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Pathways
Autonomous Smart Factories Pathway
Realtime optimisation
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
ReconCell | A Reconfigurable robot workCell for fast set-up of automated assembly processes in SMEs
11-01-2015
-28-02-2019
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
Realtime 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.
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.
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:
At the asset and machine level, the Z-BR3AK solution will perform a condition monitoring and generate health status reports. Attention will be given to the faults detection through FMEA analysis (FMECA) to allow remedial actions and synchronise the manufacturing process. The proposed engine will perform monitoring, inspection and control at component, machine, system and product level to issue warnings, alerts (e.g. about deviations from production and quality requirements), reports on (potential) failures or failure prone situations and pass related information to a higher-level Z-Bre4k DSS (for decision support at manufacturing and enterprise level).
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:
1. Augmented Reality is improving supporting processes Change over, Maintenance and Training. Partner PACE will apply their AR technology to avoid utilization of human resource in Maintenance documents handling. Instead Technologies like smart glasses and Holo lens will be applied. Virtual assistants will guide Maintenances staff through maintenance and repair processes instead. Same is targeted for Training.
2. Visualisation of machine and process data in realtime will enable immediate intervention in case of abnormal behaviour.
The acquired data is used in on-line prediction of defects. The predicted defects are used to adapt the visual quality inspection with an in-hand camera with a robot. The robot is guided to and between predetermined viewpoints associated with the predicted defects. The robot motion is generated autonomously on-line.
Thanks to this new approach with modular adaptable signal processing system and a strong interaction with data space and simulation tools trough the platform, will be possible to detect anomaly and have anequipment condition reporting , reduce reject rate by application of data-driven process model that has been derived by AI algorithms, increase OEE by recommending process adjustments to the operator or directly change the parameters in real time, so to reduce also the operator costs.
The data acquired through computer vision, is processed through machine learning algorithms and compared to an existing database of ~10000+ images already noted by human operators
Inspection results are fed into the synesis-consortium machine control platform that decides necessary changes to the machine parameters driving production in both Business Processes (1&2)
At a certain point of integration, the correlation between overall process parameters and the output product characteristics could be realized in real time in order to adjust, without any Human action, the different parameters along the production line.
This is the ideal towards which we wish to achieve with our future production lines.
Next future development and application is the automatic and intelligent retrofit excluding the current communication limits.