Project: SAFIRE
Updated at: 29-04-2024
Project: QU4LITY
Updated at: 01-02-2024
Project: QU4LITY
Updated at: 01-02-2024
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.
Project: QU4LITY
Updated at: 01-02-2024
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
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
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)
Project: QU4LITY
Updated at: 01-02-2024
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.
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.
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.
Project: QU4LITY
Updated at: 01-02-2024
Project: QU4LITY
Updated at: 01-02-2024
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.
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
Project: QU4LITY
Updated at: 01-02-2024
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 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.
Project: QU4LITY
Updated at: 01-02-2024
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.
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:
Project: QU4LITY
Updated at: 01-02-2024
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.
Project: Fortissimo 2
Updated at: 03-10-2022
Project: SYMBIO-TIC
Updated at: 29-09-2022
Project: SYMBIO-TIC
Updated at: 29-09-2022
Updated at: 26-05-2021
Over 700 data points from heating, cooling and ventilation systems are supplied to Building Management System via BACnet controllers.
IMR are using our IIoT Platform installed on-site to read this data from the BACnet controllers. We supplement it with data from IMR sensors (cleanroom occupancy, particle counts, PIRs, door sensors).
This data is then sent in real-time to a containerised cloud-based IIoT Platform where it can be accessed by the Energy Team and the Data Analytics Team.
Updated at: 26-04-2021
Based on the state of the cells and the robot's current pose an algorithm calculates the next task. The task is decomposed into a set of robot actions, navigation, manipulation or material transfer to a production cell.
There are four production cells:
Project: Fortissimo 2
Updated at: 22-03-2021
Project: Fortissimo 2
Updated at: 22-03-2021
Updated at: 09-08-2019
Updated at: 08-08-2019
Updated at: 08-08-2019
Project: TWIN-CONTROL
Updated at: 13-02-2019
Project: TWIN-CONTROL
Updated at: 13-02-2019
Project: PREVIEW
Updated at: 18-07-2018
Project: Fortissimo 2
Updated at: 23-06-2018
Project: Fortissimo 2
Updated at: 23-06-2018
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