Digital Fiber Ecosystem
Project: Digital Fibre Ecosystem
Updated at: 03-02-2022
Project: Digital Fibre Ecosystem
Updated at: 03-02-2022
Updated at: 26-05-2021
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.
ATS Bus - Enabled a single, common service bus for data exchange between the PLCs and other high level components of the system, including a SCADA system. Used a broker-based publish-subscribe approach to decouple the physical sources and destinations of the data to facilitate reconfigurability.
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
he collision avoidance software relies on the 3D models acquired by scanning of the working area. However, before the 3D model can be built the scanning results, also called “cloud of points”, are cleaned and processed.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
Project: ZDMP
Updated at: 20-05-2021
The material manufacturer stores in the platform the information concerning the production of a specific lot, including production quality control information. The work contractor is informed about the type and amount of material that are shipped to the construction side with estimated time of arrival. If some delays occur, the corresponding application running on ZDMP platform provides an assessment of the delay’s impact on the schedule and suggestions/recommendation for rescheduling.
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.
Project: ZDMP
Updated at: 20-05-2021
The quality assurance process will be supported by the ZDMP services for steel width detection, tube shape and horizontal and vertical weld of the steel sheet quality control.
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.
Project: BEinCPPS
Updated at: 22-03-2021
Project: DISRUPT
Updated at: 19-12-2019
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: 09-08-2019
Updated at: 09-08-2019
Updated at: 08-08-2019
Updated at: 08-08-2019
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.