Consistent Data – Better Processes

In the project »From conventional production plant to resilient competence plant by means of Industry 4.0 (Werk 4.0)«, scientists at Fraunhofer IPK are researching how a seamless digital process chain can be successfully implemented in production. Together with twelve partners, including TU Berlin, Mercedes Benz AG and Werner-von-Siemens Centre for Industry and Science e.V. (WvSC), they are developing flexible manufacturing solutions that will enable manufacturers to respond more quickly to technological changes and new market requirements. The basic idea: The various phases in product development, with their specific processes, tools and languages, are no longer treated as loosely connected steps. Instead, they are integrated along a digital process chain in which all the necessary data is passed on directly. This approach will enable manufacturing companies to adapt processes more flexibly, develop products more quickly and make their production more resilient overall.

The key to successful implementation lies in the digital data continuity of the information across all the involved processes and IT systems: Information should be available in precisely the form in which it is needed throughout its entire period of use. This is complicated by a number of factors in existing production systems. Information from product development, production planning, production control and manufacturing is stored and processed in different IT systems developed for the respective purpose. As a result, interdependent information, and in some cases even the very same information, exists in different data formats. If minor adjustments need to be made, for example a slight modification to a component, it takes a great deal of communication and manual data maintenance before the information can be converted into manufacturing parameters and the adjustments incorporated into the final production stage. The result can be a larger time investment, additional costs and errors.

To counteract this, a digital process chain is being developed as part of the »Werk 4.0« project that integrates all the domains involved. The researchers are applying their findings to the manufacturing process in a partner company – as a later reference for further industrial applications. This involves analyzing activities, data and the associated IT systems, identifying necessary functionalities and implementing them in technological components. To enable a high degree of generalization and transferability, they use open standards to describe, store and transfer the data.

In the first step, the respective product is described in a standardized model-based exchange format. Production parameters such as robot coordinates or geometric dimensions are then automatically derived from this product description and combined into a consistent data set. These data sets are managed by a central data management system based on IDTA (Industrial Digital Twin Association) and OPC UA (Open Platform Communications Unified Architecture) standards. A wide range of manufacturing processes, such as CNC milling, screwing, gluing or robotic stud welding, are directly connected to their respective systems, so that the stored parameters can be transferred into the running production process and the associated control software. This seamless connection – from product development to the final production process – makes it possible to carry out a wide range of process adjustments automatically: Process changes can be approved more quickly and the necessary parameters can be incorporated directly into the machine programs, across all approval and quality assurance steps.

A fully integrated digital process chain of this kind also contributes to the resilience of production systems, i.e. their ability to react to external impulses and adapt to new conditions in order to maintain a stable state. It helps to minimize communication-related misunderstandings preemptively, because unambiguous information is available and accessible throughout the entire process. The systems’ ability to react to changes is increased as well, because automated data processing and digital consistency significantly accelerate the implementation of process modifications. Since these data-based changes and their progress can be monitored centrally by all parties involved, they also enable continuous process improvements and learning effects. A real-world lab, expected to open in mid-2025, is set to grant insights into how all of this works in practice.