wbk Institute of Production Science

Marvin May, M.Sc.

  • 76131 Karlsruhe
    Kaiserstraße 12

Marvin May, M.Sc.

Area of Research:

  • Machine Learning in Production Planning and Control
  • Control of flexible Production Systems
  • Digital Twin and Matrix Production
  • Big Data Analysis in Production
  • Sim2Real: fitting simulations to real behavior
  • Knowledge Graphs in Production
  • Industry 4.0

General Tasks:

  • Coordination of lecture…
    • Production Operations Management
    • Data Mining in Production (Seminar)
    • Process Mining in Production (Seminar)
  • Learning factory on global production scalable automatization and lean & industry4.0

Projects:

  • EU Digiman4.0 
  • Innovation Center SAP – Development of an AI-based multi-agent production control for matrix production
  • BaWue Robust

Curriculum Vitae:

since 09/2019

Research Associate at the Institute of Production Science (wbk) at Karlsruhe Institute of Technology (KIT) 

2013-2019

Industrial Engineering and Management student at KIT, graduated with M.Sc. & B.Sc.

2019

Exchange Student at Shanghai Jiaotong University (上海交通大学)

2017-2018

Exchange student & Research/Teaching Associate at the University of Massachusetts, Amherst and Isenberg School of Management

2017

Exchange student at Université de Strasbourg

2016

Exchange student at Beijing Institute of Technology (北京理工大学)

12/1994

Born

Publications

[ 1 ] May, M. C.; Kuhnle, A. & Lanza, G. (2020), "Digitale Produktion und Intelligente Produktionssteuerung", wt Werkstattstechnik online, vol. 4, pp. 255-260. 10.5445/IR/1000119555
Abstract
Im Rahmen der stufenweisen Umsetzung von Industrie 4.0 erreicht die Vernetzung und Digitalisierung die gesamte Produktion. Den physischen Produktionsprozess nicht nur cyber-physisch zu begleiten, sondern durch eine virtuelle, digitale Kopie zu erfassen und optimieren, stellt ein enormes Potential für die Produktionssystemplanung und -steuerung dar. Zudem ermöglichen digitale Modelle die Anwendung intelligenter Produktionssteuerungsverfahren und stellen damit einen Beitrag zur Verbreitung optimierender adaptiver Systeme dar.

[ 2 ] Kain, M.; Miqueo, A.; May, M. C. & Häfner, B. (2020), "Metal Additive Manufacturing of Multi-Material Dental Strut Implants.". Proceedings of the 20th International Conference of the European Society for Precision Engineering and Nanotechnology, pp. S. 175-176.
Abstract


[ 3 ] May, M. C.; Overbeck, L.; Wurster, M.; Kuhnle, A. & Lanza, G. (2020), "Foresighted Digital Twin for situational Agent Selection in Production Control".
Abstract


[ 4 ] Kapp, V.; May, M. C.; Lanza, G. & Wuest, T. (2020), "Pattern Recognition in Multivariate Time Series: Towards an Automated Event Detection Method for Smart Manufacturing Systems.", Journal of Manufacturing and Materials Processing, vol. 3, [30.11.-1].
Abstract


[ 5 ] May, M. C.; Kiefer, L.; Kuhnle, A.; Stricker, N. & Lanza, G. (2020), "Decentralized Multi-Agent Production Control through Economic Model Bidding for Matrix Production Systems".
Abstract


[ 6 ] May, M. C.; Schmidt, S.; Kuhnle, A.; Stricker, N. & Lanza, G. (2020), "Product Generation Module: Automated Production Planning for optimized workload and increased efficiency in Matrix Production Systems".
Abstract