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B1 - Self-learning Engineering Assistance System

Simultaneous Development of a Self-learning Engineering Assistance System

Project Status: Active

Last Update: 14.02.2019



The objective of this subproject is the simultaneous development (viz. at the same time and parallel to the development of the new manufacturing technology) of a self-learning engineering assistance system to support product developers in an early stage in creating production-oriented sheet-bulk metal formed parts. The focus of the third phase is on the research and provision of methodologies for analyzing part designs in terms of manufacturability taking into account the influences of the entire manufacturing process chain.

Derived questions for the 3rd funding period

Main objective of the 3rd funding period: Methods for the analysis of a component design with regard to manufacturability under consideration of the influences of the entire BMU process chain are provided.

  • How can the influences of the manufacturing process and the tool design on the component properties over the entire process chain be recorded in the assistance system?
  • How can the assistance system perform the knowledge-based analysis of component and tool design locally resolved (spatially resolved)?
  • Which influences do scatters and uncertainties have on the knowledge-based analysis?
  • How can the self-learning assistance system contribute to the expansion of design or process boundaries in the identification of parameters?

Some Goals of the 3rd funding period

  • Influences of the sequence of BMU process steps and their configuration in the knowledge-based analysis of the manufacturability of components are taken into account.
  • Locally resolved metamodels are used to predict local component and tool properties under consideration of scatter and uncertainties.
  • The control variables for the expansion of the design space under consideration of two or more target variables can be identified.

Working Groups



    • Sauer, C.; Schleich, B.; Wartzack, S.: Deep learning in sheet-bulk metal forming part design. In: Marjanović D., Štorga M., Škec S., Bojčetić N., Pavković N. (Edt.): Proceedings of the DESIGN 2018 - 15th International Design Conference, (2018), Dubrovnik, pp. 2999-3010
    • Sauer, C.; Dworschak, F.; Schleich, B.; Wartzack, S.: Einsatz von Process-Mining zur Erweiterung der semantischen Produktbeschreibung. In: Krause, D.; Paetzold, K.; Wartzack, S. (Edt.): Design for X - Beiträge zum 29. DfX-Symposium, (2018), Tutzing, pp. 227-238
    • Sauer, C.; Kügler, P. ; Kestel, P.; Graf, M.; Göbel, K.; Niessen, C.; Schleich, B.; Wartzack, S.: Ein ontologiebasierter Ansatz zur Wissensrepräsentation für die smarte Produktentwicklung. In: Brökel, K.; Corves, B.; Grote, K.-H.; Lohrengel, A.; Müller, N.; Nagarajah, A.; Rieg, F.; Scharr, G.; Stelzer, R. (Edt.): 16. Gemeinsames Kolloquium Konstruktionstechnik, 1(2018)1, Bayreuth, pp. 294-305


    • Wartzack, S.; Sauer, C.; Küstner, C.: What does Design for Production mean? – From Design Guidelines to Self-learning Engineering Workbenches. In: Meyer, A.; Schirmeyer, R.; Vajna, S. (Edt.): Proceedings of the 11th International Workshop on Integrated Design Engineering, (2017), Magdeburg: Universität Magdeburg Lehrstuhl für Maschinenbauinformatik, pp. 93-102
    • Sauer, C.; Küstner, C.; Schleich, B.; Wartzack, S.: Einsatz von Deep Learning zur ortsaufgelösten Beschreibung von Bauteileigenschaften. In: Krause, D.; Paetzold, K.; Wartzack, S. (Edt.): Design for X - Beiträge zum 28. DFX-Symposium 2017, (2017), Hamburg: TuTech, pp. 49-60



      • 23.05.2018: Sauer, C.: Deep learning in der Blechmassivumformung, Dubrovnik


      • 21.02.2017: Wartzack, S.: A self-learning engineering workbench for the consideration of design of production for sheet-bulk metal formed parts within TCRC 73, Final Colloquium CRC 666 - Manufacturing Integrated Design, Darmstadt
      • 23.03.2017: Küstner, C.: Selbstlernendes Konstruktionsassistenzsystem, KTmfk Data-Mining Day 2017, Erlangen
      • 06.04.2017: Wartzack, S.: What does Design for Production mean? – From Design Guidelines to Self-learning Engineering Workbenches, Magdeburg