Project: QU4LITY
Updated at: 01-02-2024
Project: QU4LITY
Updated at: 01-02-2024
The production line in Amberg has a highly automated process with several test stations along the path.
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.
Project: QU4LITY
Updated at: 01-02-2024
AI vision algorithm developed by TNO (WP3) seems to filter bad rated parts compared to installed algorithm. Advantage can be when product print is changing to catch-up development speed in traditional algorithm development. Test-case currently in progress.
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 Kolektor Digital Platform enables us to automatically collect the data from the shop floor. The Sinapro IIoT enables the connectivity of the pilot production line machines and related IoT devices for real-time production data acquisition and monitoring. The acquired data is afterward used in the off-line machine learning pipelines to produce machine vision predictive models to detect visual injection moulding defects. A pipeline for deploying such off-line machine learning to a HPC cluster is being developed at JSI within the scope of the Kolektor Pilot.
Project: QU4LITY
Updated at: 01-02-2024
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.
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.
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 on-premise system is responsible of capturing the data from the different sensors and upload such information to the cloud. This task is performed by a software called FAGOR-DAS. Through FAGOR-DAS, data published by the sensors via PLCs using industrial protocols, such as OPC-UA, are sampled. After this data is gathered, it is analysed and compacted locally (Edge computing). Such data can be visualized via a tool called Visual Stamp (local view of the manufacturing line). The same data is prepared to be sent to the cloud infrastructure.
Project: BEinCPPS
Updated at: 29-09-2022
Project: ZDMP
Updated at: 21-05-2021
If the machine suffers major or unexpected failure, the machine is likely to be stopped. However, some other problems, such as components wearing, can lead to significant degradation in performance. In this regard, an early diagnosis of the defects and early detection of degradation signs reduces production process time. Moreover, introduction of preventive measures, in terms, for instance, of parameter adjustment, allows quality improvement and reduction of defected parts.
Project: ZDMP
Updated at: 21-05-2021
Besides the anomalies caused, for instance, by equipment degradation, this use-case targets the human error called collision. Some common collisions identified by the industrial partners are: movement of the milling head crashing into workpiece or machine itself or the CAD/CAM model defines paths involving movements that cause a crash. Collision avoidance is critical for machine damage prevention, as well as product quality maintenance.
Project: ZDMP
Updated at: 21-05-2021
The operation of machining equipment installed in production line can be optimised and improved through analysis of the process data acquired from the equipment. In some cases the optimisation process can be done by the platform. However, some cases might require involvement of engineers from equipment manufacturer to analyse data and provide recommendations on optimisation. The effectiveness of recommendations can be further assessed by the platform.
Project: ZDMP
Updated at: 20-05-2021
Project: ZDMP
Updated at: 20-05-2021
Updated at: 09-08-2019
Updated at: 09-08-2019
Updated at: 09-08-2019
Updated at: 08-08-2019
Updated at: 08-08-2019
Project: TWIN-CONTROL
Updated at: 13-02-2019
A correlation is realized within the production line between the overall process parameters and the product characteristics which are monitored at the end of the line in specific control modules.
With the help of INTRASOFT algorithm, several optimization are suggested for process parameters in order to optimize the final control workstation and to diminuate scraps and rework parts.
In addition to that, we can use CEA non-intrusive assets aquisition system to localize machine-oriented rootcauses (process deviation due to mechanical issue in the workstation for instance). This could lead to quickly identify a rootcause and to implement corrective actions effectively.
The non-intrusive notion comes from the fact that the asset monitoring (which can be a vibration from an accelerometer, a current, a temperature, ...) does not require any heavy integration, even the link with PLC is simplified with a standard exchange table for record triggers and the part informations.