CoLab4DigiTwin
Further development of cross-company 3D collaboration platforms to provide intelligent smart services that visually prepare data and provide synchronized, data-driven decision support.
The development of a digital platform for cross-company cooperation for the trusting and collaborative use of manufacturing and production data is innovative. CoLab4DigiTwin makes it possible to process complex tasks and projects across companies, time and place.
With the help of intelligent services such as XR frontend, smart data analytics and knowledge management, users are (visually) prepared investment data so that they can design their work processes along these lines. Synchronized and data-based decision-making aids support all users within their respective activities in triggering necessary processes (e.g. triggering a maintenance or service assignment) and thus making intelligent and future-oriented decisions.
The collaboration platform reduces complexity, creates transparency, increases efficiency and makes new business models possible. A prerequisite for CoLab4DigiTwin is the use of a digital twin over the entire system life cycle as a central element for the exchange of information.
Our role in the project
To ensure smooth organization and coordination as well as to ensure scientific management and exploitation, the institute takes on the overall organizational and strategic planning and management within the framework of the "Development of Smart Services".
Smart Services
Goal: Conception of the Smart Services to be developed.
After detailed requirements analysis and state-of-the-art research, the smart services are defined and designed. Within this concept, topics such as spatial computing, XR and AI-based knowledge management are addressed.
Result: Conceptual design of the individual smart services in preparation for implementation and testing.
Data Processing:
Goal: Collection and structuring of the data provided by the consortium partners. These should create the necessary basis for high data quality. Data quality analyzes are then carried out to maintain data quality within the Smart Services.
The data of the consortium partners are collected, processed and structured in a meaningful way in order to subsequently identify the necessary data quality characteristics. This creates solid data quality for use in live operations.
Result: Adjusted qualitative master data as a basis for all subsequent developments and processes.
XR front end
Goal: Conception of an XR front end to enable users to operate the three-dimensional interaction options intuitively. The cleaned data base and the IIoT sensors are then integrated into the XR environment. Finally, the XR-Fronted is being developed so that users can act on extended reality devices based on visually processed and three-dimensional information.
An XR front end is designed on the basis of a requirements analysis in order to be able to guarantee user-friendly and user-oriented visualization of the smart services. The cleaned data from the various sources (sensors, trades, subcontractors, etc.) are integrated into the XR environment via interfaces and linked to the XR front end. This is followed by the development of the XR front end for the visualization and control of the smart services on mobile and stationary extended reality devices. This includes the development of interaction mechanisms based on various tracking methods such as face, eye or hand tracking, as well as position determination within the production line.
Result: Concept and architecture of the user-friendly and user-optimized XR front end. The confluence of the data sources in the XR environment creates a user-friendly and user-optimized XR front end.
Smart Data Analytics
Goal: Conception of the Smart Data Analytics to be developed (e.g. Predictive Maintenance). Processing and merging of the data as well as visualization of the analyses. Development of intelligent algorithms for analysis. Collection of historical data and development of forecast models based on it.
The available data is collected and designed so that intelligent algorithms can be developed in the subsequent work process to carry out analyzes and design forecast models. Transfer of the results to the visual output of the XR hardware and other end devices. Based on the data sources, intelligent algorithms are developed to carry out analyzes and develop forecasts. Intelligent algorithms are developed on the basis of the historical data in order to design forecast models.
Result: Concept and architecture to perform intelligent analysis and develop forecasting models. Visually prepared data as a basis for decision-making for users. Intelligent algorithms so that users can carry out analyses. Intelligent algorithms so that users can consult forecasting models.
knowledge management system
Goal: Intelligent knowledge management is designed on the basis of a requirements analysis so that users can access information and data at any time. Development of an intelligent, user-optimized and user-friendly knowledge management system so that users or learners can call up the necessary information and data on the system at any time and regardless of the device.
An architecture for intelligent knowledge management is designed so that subsequent development can take place on this basis. Development of a user-friendly and user-optimized knowledge management system, with the help of which the users or learners have data or information about the system ready to be called up and prepared at hand.
Result: Architecture as the basis for the subsequent development work for intelligent knowledge management. Intelligent, user-optimized and user-friendly knowledge management
Quality Control
The smart services that have been designed and developed are being evaluated together with the network partner Thyssenkrupp. The result should be to ensure that the goals that are aimed for with the smart services within the overall project are achieved.
In addition, demonstrators are to be developed and implemented in the three categories of building planning, system planning and system operation, with which the three main perspectives of the platform can be validated and demonstrated.
Project reports should be written at regular intervals to document the course of the project and the results achieved. In addition, scientific publications are written and published at trade fairs and conferences. The aim here is the conscientious and complete project documentation as well as scientific publications.
The initial situation
Overview of the need for action
Previous systems for planning production plants in the automotive industry are only loosely coupled, specialized in specific applications and remain in their isolated ecosystems. The required data is usually only kept decentrally in different companies by specialists, which leads to complex and cost-intensive work processes.
The knowledge about the project is often in the heads of individual project participants. This leads to an enormous coordination effort as well as to the risk that the knowledge will leave the company together with the individual and will be lost. Another challenge is the inaccessibility of planning data due to a lack of licenses or a lack of specialist knowledge. The decentralized storage of documents results in a high maintenance effort and the error rate increases due to outdated databases. Overall, the project status is not transparent for all participants in most companies.
Required data is located decentralized in different companies
Project status often not comprehensible for those involved
Your contact person
CoLab4DigiTwin
Collaboration for Digital Twin
dr Thomas Bleistein
thomas.bleistein@aws-institut.de
+49 172 7071 475
Your contact person
CoLab4DigiTwin
Collaboration for Digital Twin
dr Thomas Bleistein
thomas.bleistein@aws-institut.de
+49 172 7071 475
Our solution approach in focus
On the basis of detailed requirement analyzes and research, user-friendly and user-optimized smart services are to be designed and developed in order to convert the 3D collaboration platform into a scalable and holistic ecosystem.
As a result, smart services are expected for the collaboration platform, which creates synchronized and data-based decision-making aids for all users. Within their respective activities, they can thus be enabled to trigger necessary processes (e.g. triggering a maintenance or service assignment) and thus make the best possible decisions. In addition, new business models can be derived on this basis.
In particular, the creation of an XR user interface enables users to use engineering, manufacturing and production data with visual support and to embed them in everyday work within workflows. Furthermore, increases in efficiency can be achieved through the analysis of historical data and the development of forecast models. In addition, an intelligent knowledge management system offers the possibility of extracting relevant information for employees without a technical background and thus making a contribution against the shortage of skilled workers.
A core challenge with CoLab4DigiTwin can be the collection and structuring of the various data sources (e.g. through consortium partners or IIot sensors) and the subsequent creation of data quality. If this is insufficient, a meaningful and intelligent development of forecast models can be inhibited.
Funding notice
The CoLab4DigiTwin project is funded by the Federal Ministry for Economic Affairs and Energy.
Funding code: 13IK013F
Running time: 01.01.2023-31.12.2025
