Relevant:

Associated Results

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.

The gathering of data from the machines during their lifetime allows  the generation of valuable information for the improvement, not only of the actual machine and process, but also the future machines and smart functions that are continously improved through data based engineering and design. 

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).

  • Results:

    The Beyond platform implemented provides valuable information with daily, monthly reports and deeper analysis in concrete operations that allows technicians to optimize the machine (industrial furnace) operation.

    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.

    Analyzing the test and process data, specific machine parameters can be adapted and optimized.

    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.

    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.

    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.

  • Results:

    With the quality management platform, optimization of production is enabled.

    The optimization of quality process decision is taking place thank to a holistic view of the factors that influence the perception of the quality from the consumer prespective. The platform using a MPFQ driven data model is enabling a faster, more reliable and flexible visualization system and analytical approach.

    There are two IoT platforms included in 2 manufacturaing lines for Automotive and Railway sector for MONDRAGON pilot. IoT platforms monitor 3 grinding machines from railway sector as well as press machine, stacker, Owen and Transfer from Automiotive customer. 

    The variables monitored allow machine tool buider to know that the process exectuion is under treshold defined as well as enabling predictions about possible faillures. As a consequence there will be an increase of the quality production and the optimisation of the production. 

    The interoperability layer between two IoT Platfroms has been achieved considering OPC-UA and AAS.

    Collaboration with international partners such as ATLANTIS, VTT and FHG has allowed us to include IA algorithms, specific data analitics and data sharing connectors.In spte of the fact that IoT platfrom and interopoerability has been oriented to real time optimisation the analitycs and the IA algorithms have been carryed out off-line

     

    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.