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M.Sc. Lukas Weiser

Research Associate
department: Production Systems
office hours: to be agreed
room: 105, Geb. 50.36
phone: +49 1523 9502629
lukas weiserXdc4∂kit edu

76131 Karlsruhe
Kaiserstraße 12

M.Sc. Lukas Weiser

Area of Research:

  • Quality assurance in additive manufacturing
  • Process-integrated measurement technology
  • Sensor development for LPBF process monitoring
  • Machine Learning / Artificial Intelligence
  • MES development
  • Leightweight construction: hybrid fibre composites


General Tasks:

  • Coordination of lecture Production & Operations Management (POM)
  • Coordinator Learning Factory Module 3 – Six Sigma
  • Core Team Learning Factory - Software


Curriculum Vitae:

since 11/2018 Research Associate at the Institute of Production Science (wbk) at Karlsruhe Institute of Technology (KIT)  
10/2012 - 10/2018 Study of mechanical engineering at Karlsruhe Institute of Technology (KIT)  
14/07/1993 Born in Bühl  



[ 1 ] Eschner, N.; Weiser, L.; Häfner, B. & Lanza, G. (2018), "Development of an Acoustic Process Monitoring System for the Selective Laser Melting (SLM)". Solid Freeform Symposium - Proceedings, eds. SFF Symposium, pp. 2097-2117.
The current selective laser melting (SLM) process lacks both process quality and reproducibility. Recent research focuses on the integration of optical measuring technology, but acoustic sensors also seem promising. Initial results on acoustic methods show their suitability. The further processing of the data still shows difficulties, mostly due to the high sample rate. In this work a concept for an acoustic process monitoring system is developed and integrated into the process. First results show its capability to distinguish different process qualities. For this purpose, various configurations for in-process integration of acoustic measurement techniques are discussed and evaluated. The most promising structure-borne sound concept is integrated and tested in a test bed. In a Design of Experiments for specific parameter selection, cubes with different process qualities are produced, and the acoustic signatures are evaluated. For a first prove of concepts a Neuronal Network is trained to classify three different laser classes. Therefore, different NN topologies were tested and the best-found solution had a precision of more than 90%.