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Technical University of Munich
Picture of Daniel Piendl

Daniel Piendl, M.Sc.

Technical University of Munich

Postal address

Boltzmannstr. 15
85748 Garching b. München

Department Machine Tools

Phone: +49 (89) 289 - 15586
Email: Daniel.Piendl(at)iwb.tum.de

Research topics

  • Condition monitoring and predictive maintenance of machine tool components
  • Protection of sensitive machine data using privacy-enhancing technologies
  • Collaborative (cross-company) training of AI models for tool condition monitoring
  • Systematic acquisition, processing and storage of machine tool data using edge devices, data interfaces, dashboards and databases

2025

  • Jordan, Patrick; Piendl, Daniel; Kroeger, Sebastian; Streibel, Lasse; Haider, Christoph; Zaeh, Michael F.: Enabling the material flow analysis in disassembly systems using object-centric process mining. Procedia CIRP 134, 2025, 271-276 more…
  • Piendl, Daniel; Ludwig, Andy; Rothe, Jakob; Zäh, Michael F.: Kollaboratives Modelltraining und Datensicherheit. Zeitschrift für wirtschaftlichen Fabrikbetrieb 120 (s1), 2025, 214-218 more…
  • Piendl, Daniel; Radtke, Maximilian-Peter; Jacobsen, Hendrik; Bock, Jürgen; Zaeh, Michael F.: Predictive Maintenance of Ball Screws: A Comparative Study Using Real-World Industrial Data. Procedia CIRP 134, 2025, 390-395 more…
  • Piendl, Daniel; Radtke, Maximilian-Peter; Jacobsen, Hendrik; Bock, Jürgen; Zaeh, Michael F.: Predictive Maintenance of Ball Screws: A Comparative Study Using Real-World Industrial Data. Procedia CIRP 134, 2025, 390-395 more…

2023

  • Ellinger, Johannes; Piendl, Daniel; Zaeh, Michael F.: Comparison of Sensitivity-Guided and Black-Box Machine Tool Parameter Identification. Journal of Manufacturing and Materials Processing 7 (4), 2023, 120 more…
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