Project: SAFIRE
Updated at: 29-04-2024
Project: COMPOSITION
Updated at: 29-04-2024
Project: QU4LITY
Updated at: 01-02-2024
We are developing Sinapro IIoT MES/MOM cloud solution (part of the Kolektor Digital Platform) as the cornerstone of the MOM system which enables real-time collecting, evaluating, validating, filtering, checking, and storing of production data. The captured production data can be processed in real-time for the purpose of obtaining various production information, which enables immediate action. MOM function for production analyses with depth learning technology of AI gives users additional and high-quality information’s for fast decisions to achieve zero-defect goals in production.
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: Digital Fibre Ecosystem
Updated at: 03-02-2022
Updated at: 26-05-2021
IMR IIoT Toolkit developed for factory deployment. High voltage power components separated in custom enclosures.
It includes a range of sensors, IIoT edge components which perform data collection and aggregation at the edge of the network and which then sends the data to IMR’s IIoT Platform which can either be installed locally, be based in the cloud or even both.
IIoT Toolkit software connectors allow interfaces to be established with operational technologies such as BMS over BACnet or EMS over HTTP.
Updated at: 26-05-2021
Integration of each subsystem and subsystem components is done via AAS data models and applications. Each individual inspection station component is represented by its own AAS which provides relevant information about asset as well as the control interface (where applicable). AAS application can communicate to each other via OPC-UA communication protocol.
The main goal of the station is to demonstrate how the AAS architecture could be implemented, what are the benefits, what still needs to be addressed.
Siemens PLM XML - Data standard used to define process data from Teamcenter and send to the SCADA and to PLCs.
Project: ZDMP
Updated at: 21-05-2021
The sensors deployed on the FORM side are used to aquire the process and the equipment data. These data are sent and stored on the ZDMP platform that is used to detect the abnormalities and failures right after they occur and immediately inform the operator, but also to be able to predict and avoid further malfunctions. The components of the ZDMP platform are used to detect any deviations from the normal production process.
The parameters of each manufacturing operation are reported to the ZDMP platform. Within ZDMP platform the parameters are analysed to identify, if selected parameters will result in the good quality and if not, how the parameters can be changed.
Project: ZDMP
Updated at: 21-05-2021
Project: ZDMP
Updated at: 21-05-2021
The X-Ray machine will be deployed at the CONT factory for quality analysis improvement and in-time defects detection. The analysis will be applied to materials and components used within the production process. Before the process start, machine requests the inspection program from ZDMP platform, if one is available, the process starts automatically.
Project: ZDMP
Updated at: 21-05-2021
Usually the assembling of electronic components within the CONT is performed using 6-11 working stations. AS the workstations can be from different manufacturers and have no direct connection, the goal of ZDMP platform is to provide a needed middleware and services for centralized assembly line control by acquiring data from different workstations.
Project: ZDMP
Updated at: 21-05-2021
The test check stations along the assembly line equipped with the cameras serving the goal of optical quality control. Data in the form of images taken within these check stations is a valuable resource that is used not only to check the quality of product, but also to improve the efficiency of quality testing programs. The images taken allow detecting, for instance, defects related to the shape of the product.
Project: ZDMP
Updated at: 21-05-2021
The machine centres operating within the plant are equipped with sensors (e.g. controlling vibrations, power consumption, etc.) supplying the process data. On the other hand, industrial computers controlling the machine also provide additional information about production process, such as process times, machine status and cylinder block type in production. All these data are captured and stored within the database to be further analysed on abnormalities and to provide recommendations on changing of certain parameters to recover production process.
Project: ZDMP
Updated at: 20-05-2021
To be able to make prediction and automated quality assessment, process data need to be gathered and presented in the form suitable for processing. Process data are gathered from various sensors and smart meters, as well as from PLCs at MRHS and automatically uploaded to the database. As the production cycle takes around 2 minutes, subsequently data are uploaded every 2 minutes. The ultimate goal is to receive the anomaly warnings close to real-time.
Project: ZDMP
Updated at: 20-05-2021
ZDMP platform has the goal to improve and automate the quality check on every stage of the stone slabs and tiles production. Reduce, where possible, the human involvement in the quality check to minimum, e.g. control of the wearing out of the cutting blades. Both the data about equipment performance, as well as material scanning data are utilized. Moreover, CEI machines also provide the data from cameras and projectors used to optimize the cutting process and save material.
Project: ZDMP
Updated at: 20-05-2021
Project: DYNXPERTS
Updated at: 22-03-2021
Project: BEinCPPS
Updated at: 22-03-2021
Project: SatisFactory
Updated at: 21-11-2019
Updated at: 09-08-2019
Updated at: 09-08-2019
Updated at: 09-08-2019
Updated at: 09-08-2019
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
Updated at: 08-08-2019
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.