Data integration and data structures – realization of data continuity via the digital twin

In the course of digitization and networking, production systems and products are being connected to the digital world and are generating ever larger amounts of data in the Industrial Internet of Things (IIoT).

In different product life cycle phases, different data is created: material flow, product and process data in the production are mainly generated by sensors from machine tools or measuring systems. Different machines and systems generate data that can differ in their formats and structures. In addition, the data is generated by various partners from the value chain.

Concrete goals of data processing can be determination, prognosis, prediction or optimization of quality and performance. For this purpose, both the availability and usability of data between process steps, departments and companies must be guaranteed.

However, data consistency in process chains, within the company or even beyond company boundaries often does not yet exist. Obstacles to this data consistency are the variety of data formats and structures as well as a high degree of complexity in the aggregation and synchronization of data. In order to exploit the existing potential, it is necessary to merge, cleanse, transform and finally integrate data from several and different sources.

Fraunhofer IPT has been working for years on data consistency in manufacturing process chains and supports companies in its implementation. One focus of the research activities is on the digital twin. On the one hand, this concept comprises the creation of data consistency in process chains as well as in cross-company value-added networks and over the entire product life cycle. On the other hand it includes the use of the database for applications such as predictive maintenance or real-time process optimization.

Our range of services

• Concepts for data consistency in process chains and cross-company value chains
• Studies on the preparation of data for data-based projects such as predictive maintenance, real-time process optimization or digital twins