Prof. Dr.-Ing. Benjamin Schleich

Contact

work +49 6151 16-21791
fax +49 6151 16-21793

Work L1|01 10
Otto-Berndt-Straße 2
64287 Darmstadt

Prof. Dr.-Ing. Benjamin Schleich received his doctorate from the Faculty of Engineering at Friedrich-Alexander-Universität Erlangen-Nürnberg in 2017. Subsequently, he was senior engineer and research area manager for virtual product development and design methodology at the Chair of Engineering Design at Friedrich-Alexander-Universität Erlangen-Nürnberg. Since September 1, 2022, he has been professor and head of the Institute for Product Life Cycle Management (PLCM) in the Department of Mechanical Engineering at Darmstadt University of Technology. Prof. Dr.-Ing. Schleich is also an elected member of the Advisory Board of the Design Society, Research Affiliate of the International Academy for Production Engineering (CIRP) and head of the Data Management working group of the DFG SFB/Transregio 285. His research interests include digital engineering, digital twins, data mining and machine learning in product development, tolerance management and robust design (esp. in the context of Industry 4.0) as well as innovative methods and CAx process chains.

Prof. Dr.-Ing. Benjamin Schleich received his doctorate from the Faculty of Engineering at Friedrich-Alexander-Universität Erlangen-Nürnberg in 2017. Subsequently, he was senior engineer and research area manager for virtual product development and design methodology at the Chair of Engineering Design at Friedrich-Alexander-Universität Erlangen-Nürnberg. Since September 1, 2022, he has been professor and head of the Institute for Product Life Cycle Management (PLCM) in the Department of Mechanical Engineering at Darmstadt University of Technology. Prof. Dr.-Ing. Schleich is also an elected member of the Advisory Board of the Design Society, Research Affiliate of the International Academy for Production Engineering (CIRP) and head of the Data Management working group of the DFG SFB/Transregio 285. His research interests include digital engineering, digital twins, data mining and machine learning in product development, tolerance management and robust design (esp. in the context of Industry 4.0) as well as innovative methods and CAx process chains.