EmuDig 4.0


As part of the EMuDig 4.0 project (“Efficiency boost in massive forming through the development and integration of digital technologies in engineering along the entire value chain”) funded by the BMWi, an application-oriented solution is to be developed, tested and evaluated with which the data of complex production systems along the entire value chain (see adjacent figure) can be recorded, stored, processed and analyzed. This will address the introduction of digital technologies and the networking of production systems in the massive forming sector, on the basis of which data-driven approaches to increasing process stability, product quality (‘predictive quality’) and optimizing maintenance (‘predictive maintenance’) will be implemented. This should enable improved end-to-end product engineering and more efficient production. In an interdisciplinary cooperation of companies from the solid forming industry, plant manufacturers and university research institutes, the overall plant efficiency is to be significantly increased. The overall project is composed of 6 subprojects, which are listed below with the respective project leaders:

  • TP1 Raw material production aluminum (Otto Fuchs KG) and steel (Hirschvogel Automotive)
  • TP2 Forming process (Institute for Forming Technology, University of Stuttgart)
  • TP3 Production equipment (SMS group)
  • TP4 Production tools (Laboratory for Solid Forming, FH Südwestfalen)
  • TP5 Logistics process (Institute for Automation Technology and Software Systems, University of Stuttgart)
  • TP6 Factory Cloud (Center for Information Services and High Performance Computing, TU Dresden)

The IAS is involved in the overall project in the requirements analysis, the development of concepts and methods of real-time data acquisition and through the implementation of software solutions. The focus here is on the acquisition and provision of real-time data, the structuring, modeling and analytical consideration of this data as well as the feedback of control data and recommendations for action to increase the quality in massive forming processes. In this way, the IAS is to realize logistical control and traceability of “smallest possible partial quantities” of an entire production batch.

Based on the extensive data landscape along the multi-layered process chain of massive forming, a component for data preparation will be implemented that provides the process data in a uniform manner. Subsequently, the prepared data will be structured and managed in a real-time database. Ad-hoc analyses are to be used to predict and compensate for short-term fluctuations in product and process quality. The results are to be visualized to the worker at the plant on a mobile device or glasses in the form of an assistance system. The prototypical implementation and the proof of technical and economic feasibility will be carried out at the Institute of Forming Technology. Furthermore, an exemplary realization and validation of the achieved results will be carried out at the two pilot plants of the industrial partners Otto Fuchs and Hirschvogel.

Nasser Jazdi, PhD
Nasser Jazdi, PhD
Academic Senior Councilor & Academic Director | Deputy Director | Visiting professor at the Anhui University | IEEE Senior Member

Dr. Nasser Jazdi is an electrical engineer and researcher with 20+ years of experience.