MUSIC | MUlti-layers control&cognitive System to drive metal and plastic production line for Injected Components
01-09-2012
-31-08-2016
Projects overview
11 items found
FACTS4WORKERS | Worker-Centric Workplaces in Smart Factories
01-12-2014
-30-11-2018
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Pathways
Autonomous Smart Factories Pathway
Connected IT and OT
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.
HORSE | Smart integrated Robotics system for SMEs controlled by Internet of Things based on dynamic manufacturing processes
11-01-2015
-31-07-2020
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Pathways
Connected IT and OT
OPTIMISED | Operational Planning Tool Interfacing Manufacturing Integrated Simulations with Empirical Data
11-01-2015
-31-10-2018
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.
Result items:
Through visual analytics
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
Result items:
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 machines (OT) are connected to intelligence in the Edge and Cloud (IT) for generation of Zero Defect Manufacturing functions
For many years, and in the context of INDUSTRY 4.0, FAGOR ARRASATE is working together with IKERLAN in smart platform for press machines and industrial processes. The platform goes from the sensitization of the machine’s critical elements to the remote monitoring of press conditions. The platform focuses on improvement of asset management and OEE (Overall Equipment Effectiveness) and allows FAGOR ARRASATE to increase quality of service for their clients.
Result items:
Operators are connected to the smart funcions the receive valuable information related to components condition that allow them to take decisions related to machine and process. Maintenance technitians and specialized engineers from the machine tool provider can also be included in the process.
Operators are more involved in the whole process, but mainly receives valuable information of the furnace operation through the real-time monitoring interface of the platform. Also daily reports provide them valuable information.
The Kolektor Digital Platform enables human involvement on various levels. Human operators can monitor, view and inspect created datasets. During the process of model training, the operator can monitor the current state and detailed information of the training process. The Kolektor Digital Platform opens a channel between a data scientist and a decision-making individual in the production line. It is desirable to have multiple people, each assigned to a specific task. The whole process could be split into subtasks - acquiring images on the production line, human expert labeling the images (classification, anomaly,..), data scientist training the model on the new dataset and at the end evaluation of the model and pushing the new (improved) model to the production line.
The RiaStone Qu4lity Pilot enables human involvement at various levels and production stages.
Human operators inspect, monitor, and view all created datasets. During the process of model training, the operator guides the ML algorithm through the image inspection training process.
This is performed after the image acquisition in the production line, through joint algorithm/human expert labeling of the images (new defect classification, conformant/non-conformant product), this allowing for the training of the algorithm in the existing defects dataset, and at evolving the algorithm and pushing the new (improved) model to the production line AQL platform
ZDMP | Zero Defect Manufacturing Platform
01-01-2019
-30-06-2023
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Result items:
Result items:
In the case of the negative automatic test, operator performs the manual check of the product comparing it with the reference images. The operator decisions with corresponding images are collected and stored to learn or extract the defects types and acceptance limits.
The assembly process is mostly automated. However, some manufacturing stages, as well as some quality operations are performed manually. In this regard, the production process can be improved when quality and performance details can be delivered in time and to the right person.
Each user will have different levels of interaction with the ZDMP platform. Both contractor and supervisor should have access to the construction schedule, but their own task schedules should only be accessible to each of them. Similarly, the Supplier will not have access to the Supervisor or Works Contractor’s areas and vice-versa.
After the inspection process finished, a report is produced and if the product or material corresponds to the specifications the production process continues, but if some deviations are detected, report is sent to the operator for detailed check.
In some cases, FORD production engineer has to contact machine builder to get the recommendations on improving the machining process, while sending the process data to the equipment manufacturer. On the EXTE side the data undergo further analysis to provide recommendation on production process improvement. The recommended actions are manually introduced into the ZDMP platform. Afterward, the platform can assess the effectiveness of provided recommendations and improve its knowledge base.
The operator is involved in the working area scanning process. Afterwards the scanning results can be automatically sent to the ZDMP platform to be further processed and converted into the .stl format.
Through utilization of ZDMP platform, the operator will get a notification, if a defect is detected. This releases the quality operator from their cursory monitoring task, and it is moved into a reactive role. Before, the operator was in charge for manual detection of possible defects. In its turn, ZDMP platform has the goal to reduce the load on operator and make the production process and quality control more self-reliant.
One of the goals of the use-case is to minimize the human involvement in the quality assessment process. However, it is not always feasible, as for instance to detect natural defects of material (stone), but still operator can get significant assistance from ZDMP platform and corresponding services to automatically detect some defects.
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Pathways
Autonomous Smart Factories Pathway
Connected IT and OT
Result items:
COALA | COgnitive Assisted agile manufacturing for a LAbor force supported by trustworthy Artificial Intelligence
01-10-2020
-30-09-2023
Next future development and application is the automatic and intelligent retrofit excluding the current communication limits.