K
Konrad Wegener
Researcher at ETH Zurich
Publications - 552
Citations - 11455
Konrad Wegener is an academic researcher from ETH Zurich. The author has contributed to research in topics: Machining & Machine tool. The author has an hindex of 42, co-authored 486 publications receiving 7959 citations. Previous affiliations of Konrad Wegener include University of Zurich & École Polytechnique Fédérale de Lausanne.
Papers
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Analysis of The Chip Formation of Bainitic Steel in Drilling Processes
TL;DR: In this article, a non-destructive method was used for characterization of chip formation based on chip roots, and a quick stop device was developed to generate real chip roots in order to verify the machinability of bainitic steels.
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Autonomous and data-efficient optimization of turning processes using expert knowledge and transfer learning
TL;DR: In this paper , the number of experimental trials for finding optimal process parameters is reduced by incorporating expert knowledge and transferring knowledge between different tasks, where the turning process costs are modeled using Gaussian process models and the selection of informative experiments is achieved by Bayesian optimization.
Embedding fibre optical sensors into SLM parts
Philipp Stoll,Jinesh Mathew,Adriaan B. Spierings,Thomas Bauer,Robert R. J. Maier,Konrad Wegener +5 more
TL;DR: In this paper, the integration of fiber optical sensors into SLM manufactured coupons in a commercial, industry scale, SLM machine is discussed, as well as the corresponding bonding quality.
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A conceptual vision for a bio-intelligent manufacturing cell for Selective Laser Melting
Konrad Wegener,Adriaan B. Spierings,Roberto Teti,Alessandra Caggiano,D. Knüttel,Alexandre Staub +5 more
TL;DR: A visionary concept of an SLM system integrating functionalities and behaviors of the operator is presented, and embeds it into a conceptual framework for an intelligent SLM process chain that is capable of self-optimization.
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Spark location adaptive process control in meso-micro EDM
TL;DR: In this article, a correlation between the discharge voltage and the geometric location of a discharge on an electrode has been established, and a microsecond level spark location adaptive process control has been conceptualized and demonstrated.