PROF. DR. MICHAEL MÖCKEL

Möckel, Michael | Intelligent Systems, Materials

Research Area:
Intelligent Systems, Materials

Aschaffenburg University of Applied Sciences, Germany
Faculty: Engineering

E-Mail: Michael.Möckelth-abde
Phone: +49 6021/4206-507

Areas of Expertise



Research Labs & Institutes


Research Projects

KAnIS Autonomous Intralogistric Systems

KAnIS Autonomous Intralogistric Systems

ArtificialIntelligenceDataSciences   ElectronicElectricDrives   RoboticsAutomation   IntelligentMobility   IntelligentSensorsSignals

Network and automate industrial trucks in order to optimize profitability, efficiency and safety. Development and application of interdisciplinary methods and tools.
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EpiLABKI

EpiLABKI

ArtificialIntelligenceDataSciences   InnovativeMaterialProcessing   CompetenceCenterAI

The EpiLABKI project creates an epidemic simulator for the Aschaffenburg region using artificial intelligence.

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KIProBatt

KIProBatt

ArtificalIntelligenceDataScience  IntelligentSensorsSignals  MaterialTestingSensorTechnology InnovativeMaterialProcessing  CompetenceCenterAI

Intelligent battery cell production with AI-supported process monitoring based on a generic system architecture.
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PromoAdd3D Additive Manufacturing

PromoAdd3D Additive Manufacturing

RoboticsAutomation   ArtifialIntelligenceDataScience   IntelligentSensorsSignals   MaterialTestingSensorTechnology   InnovativeMaterialProcessing

Improvement of SLM processes designing a hybrid model using effective parameters.

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Curriculum Vitae

since 2015Aschaffenburg UAS, GermanyProfessorship
2012-2015University of Cambridge, UK Research Associate
20011-2012Fraunhofer Institute, GermanyScientist
2009-2011Max-Planck-Institute, GermanyPost-Doc
2006-2009Ludwig-Maximilians-Universität München, GermanyScientific Employee
2006-2007University of Augsburg, GermanyScientific Employee
1999-2009Ludwig-Maximilians-Universität München, GermanyDiploma, Doctorate (Solide state physics)
2002-2003University of Cambridge, UK Master Mathematics


Publications

  • Voigt, Jorrit, Möckel, Michael, 2021. Comparing principal component analysis (PCA) and 𝛃-variational autoencoder(𝛃-VAE)for anomaly detection in selective laser melting (SLM) process data. In: 14th WCCM-ECCOMAS Congress 2020, virtual congress, January, 11-15, 2021. S. 1 – 9. Abstract
  • Pfenning, Stefan, Döhring, Thorsten, Möckel, Michael, 2019. Einzelphotonenquellen – Schlüsselkomponenten für die Quantenwelt. In: DGaO-Proceedings. DGaO, S. 1 – 2. ISSN 1614-8436. Abstract

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