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M.Sc. Lukas Weiser

Akad. Mitarbeiter
Bereich: Produktionssysteme
Sprechstunden: nach Vereinbarung
Raum: 105, Geb. 50.36
Tel.: +49 1523 9502629
lukas weiserSwd6∂kit edu

76131 Karlsruhe
Kaiserstraße 12


M.Sc. Lukas Weiser

Forschungs- und Arbeitsgebiete:

  • Qualitätssicherung in der additiven Fertigung
  • Prozessintegrierte Messtechnik
  • Sensorentwicklung zur LPBF-Prozessüberwachung
  • Maschinelles Lernen / Künstliche Intelligenz
  • MES-Entwicklung
  • Leichtbau: hybride Faserverbunde

 

Allgemeine Aufgaben:

  • Vorlesungsbetreuer der Vorlesung BPW (Betriebliche Produktionswirtschaft)
  • Koordinator Lernfabrik Modul 3 – Six Sigma
  • Kernteam Lernfabrik - Software

 

Lebenslauf:

seit 11/2018 Wissenschaftlicher Mitarbeiter am Institut für Produktionstechnik (wbk) des Karlsruher Instituts für Technologie (KIT)  
10/2012 - 10/2018 Studium des Maschinenbaus am Karlsruher Institut für Technologie (KIT)  
14/07/1993 Geboren in Bühl  

 

Veröffentlichungen

[ 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, Hrsg. SFF Symposium, S. 2097-2117.
Abstract:
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%.