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Lucas Bretz

M.Sc. Lucas Bretz

Akad. Mitarbeiter
Bereich: Produktionssysteme
Sprechstunden: nach Vereinbarung
Raum: 108, Geb. 50.36
Tel.: +49 1523 9502567
Fax: +49 721 608-45005
Lucas BretzJds6∂kit edu

76131 Karlsruhe
Kaiserstraße 12


M.Sc. Lucas Bretz

Forschungs- und Arbeitsgebiete:

  • Qualitätssicherung im Leichtbau
  • Automatische Sichtprüfung
  • Machine Learning in der Qualitätssicherung
  • Struktursimulation (FEM)
  • Industrie 4.0

 

Allgemeine Aufgaben:

  • Vorlesungsbetreuung Lernfabrik – Globale Produktion
  • Vorlesungsbetreuung Qualitätsmanagement
  • Vorlesungsbetreuung Praktikum Produktionsintegrierte Messtechnik

 

Projekte:

  • IRTG - International Research Training Group – Integrierte Entwicklung kontinuierlich-diskontinuierlich langfaserverstärkter Polymerstrukturen
  • EU Inline - An innovative design of a flexible, scalable, high quality production line for PEMFC manufacturing
  • SPP1712 – Intrinsische Hybridverbunde für Leichtbaustrukturen

 

Versuchsstände:

 

Lebenslauf:

seit 06/2018 Wissenschaftlicher Mitarbeiter am Institut für Produktionstechnik (wbk) des Karlsruher Instituts für Technologie (KIT) 
10/2017 - 03/2018 Auslandsaufenthalt am Global Advanced Manufacturing Institute (GAMI) in Suzhou, China
10/2012 - 04/2018  Studium des Maschinenbaus am Karlsruher Institut für Technologie (KIT)
15/12/1992 geboren in Dernbach, Westerwald

 

Veröffentlichungen

[ 1 ] Fengler, B.; Schäferling, M.; Schäfer, B.; Bretz, L.; Lanza, . G.; Häfner, B.; Hrymak, A. & Kärger, L. (2019), „Manufacturing uncertainties and resulting robustness of optimized patch positions on continuous-discontinuous fiber reinforced polymer structures“, Composite Structures, S. 47-57. https://doi.org/10.1016/j.compstruct.2019.01.063
Abstract:
Discontinuous fiber-reinforced Sheet Moulding Compound (SMC) in combination with continuous carbon fiber patches provide a high design freedom in combination with good weight-specific properties. However, the application of these materials requires a strategic and exact positioning of the patches, necessitating the consideration of unavoidable manufacturing defects during the design phase. Therefore, a workflow is proposed to evaluate the robustness of multi-objective patch optimization results using two robustness measures, the degree of robustness and robustness index. An efficient calculation of the robustness measures is achieved by replacing computational expensive simulation models with a Kriging surrogate model. Typical manufacturing deviations occurring during the patch positioning and moulding process are determined from experiments using active thermography. Finally, the proposed workflow is applied to the multi-objective optimization of two patches on a demonstrator under four-point-bending load. The resulting robustness measures can be used as a decision criterion for the selection of the best Pareto optimal solution. Furthermore, they can be used for the determination of the maximum occurring objective variation as well as the largest permissible manufacturing tolerances.