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Lucas Bretz

M.Sc. Lucas Bretz

Research Associate
department: Production Systems
office hours: to be agreed
room: 108, Geb. 50.36
phone: +49 1523 9502567
Lucas BretzGlk8∂kit edu

76131 Karlsruhe
Kaiserstraße 12

M.Sc. Lucas Bretz

Area of Research:

  • Quality Assurance in Lightweight Construction
  • Automatic visual inspection
  • Machine Learning in Quality Assurance
  • Structural simulation (FEM)
  • Industry 4.0


General Tasks:

  • Coordination of lecture Learning Factory Global Production
  • Coordination of lecture Quality Management
  • Coordination of lecture Laboratory production integrated metrology



  • 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 - Intrinsic hybrid composites for leightweight structures


Test benches:


Curriculum Vitae:

since 06/2018 Research Associate at the Institute of Production Science (wbk) at Karlsruhe Institute of Technology (KIT) 
10/2017 - 03/2018 Stay abroad at the Global Advanced Manufacturing Institute (GAMI) in Suzhou, China
10/2012 - 04/2018  Study of Mechanical Engineering at Karlsruhe Institute of Technology (KIT)
15/12/1992 Born in Dernbach, Westerwald



[ 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, pp. 47-57. https://doi.org/10.1016/j.compstruct.2019.01.063
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.