Through the lens of Generative AI: The expressiveness of research on validation techniques for material flow simulation

Research output: Contribution to journal › Conference article › Contributed › peer-review

Contributors

  • Heiner Reinhardt - , Chemnitz University of Technology (Author)
  • Mahtab Mahdaviasl - , Fraunhofer Institute for Machine Tools and Forming Technology (Author)
  • Bastian Prell - , Technical University of Applied Sciences Wildau (Author)
  • Steffen Ihlenfeldt - , Fraunhofer Institute for Machine Tools and Forming Technology (Author)
  • Lothar Kroll - , Chemnitz University of Technology, AGH University of Science and Technology (Author)
  • Matthias Klärner - , Chemnitz University of Technology (Author)

Abstract

In the light of the ongoing development of Generative AI (GenAI), we encourage the reflection on scientific publishing to increasingly leverage current and future information processing capabilities. Here, we propose a commonly recognizable framework for the description of validation techniques applicable to the material flow simulation of manufacturing systems. By utilizing this framework not only the explanation of such techniques can be structured appropriately but also a form of guidance for GenAI-driven information processing can be provided. Illustrated by the use case of a high-volume automotive production line, we demonstrate the feasibility of the framework as we streamline the description of two different validation techniques. In doing so, the application of the framework to support the information processing capabilities of GenAI-driven services is discussed.

Details

Original languageEnglish
Pages (from-to)451-456
Number of pages6
JournalProcedia CIRP
Volume146
Publication statusPublished - Sept 2026
Peer-reviewedYes
Externally publishedYes

Conference

Title59th CIRP Conference on Manufacturing Systems
SubtitleDriving the Future of Manufacturing Systems with AI and Sustainability
Abbreviated titleCIRP CMS 2026
Conference number59
Duration19 - 21 May 2026
Website
LocationAT&T Conference Center
CityAustin
CountryUnited States of America

Keywords

Keywords

  • Digital manufacturing system, Digital Twin, Generative AI (GenAI), Optimization, Simulation