Materials Map

Discover the materials research landscape. Find experts, partners, networks.

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The Materials Map is an open tool for improving networking and interdisciplinary exchange within materials research. It enables cross-database search for cooperation and network partners and discovering of the research landscape.

The dashboard provides detailed information about the selected scientist, e.g. publications. The dashboard can be filtered and shows the relationship to co-authors in different diagrams. In addition, a link is provided to find contact information.

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Materials Map under construction

The Materials Map is still under development. In its current state, it is only based on one single data source and, thus, incomplete and contains duplicates. We are working on incorporating new open data sources like ORCID to improve the quality and the timeliness of our data. We will update Materials Map as soon as possible and kindly ask for your patience.

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in Cooperation with on an Cooperation-Score of 37%

Topics

Publications (2/2 displayed)

  • 2022Steuerung von Compliant-Mechanismen durch Reinforcement Learningcitations
  • 2022Melt Spinning of Elastic and Electrically Conductive Filament Yarns and their Usage as Strain Sensors1citations

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Chart of shared publication
Muschalski, Lars
1 / 4 shared
Hornig, Andreas
1 / 47 shared
Modler, Nils
1 / 355 shared
Cherif, Chokri
1 / 112 shared
Probst, Henriette
1 / 3 shared
Nocke, Andreas
1 / 34 shared
Mersch, Johannes
1 / 9 shared
Chart of publication period
2022

Co-Authors (by relevance)

  • Muschalski, Lars
  • Hornig, Andreas
  • Modler, Nils
  • Cherif, Chokri
  • Probst, Henriette
  • Nocke, Andreas
  • Mersch, Johannes
OrganizationsLocationPeople

document

Steuerung von Compliant-Mechanismen durch Reinforcement Learning

  • Muschalski, Lars
  • Hornig, Andreas
  • Modler, Nils
  • Wollmann, Joanna
Abstract

Controlling of compliant-mechanisms with reinforcement learning Driving compliant-mechanisms to target positions is particularly challenging since it is not or hardly possible to set up the inverse kinematics with analytical models. On the basis of an exemplary compliant-mechanism, this work shows how machine learning methods can be applied to successfully learn the corresponding kinematics. This allows statements on how the actuators have to be controlled in order to reach arbitrary points with the mechanism.

Topics
  • impedance spectroscopy
  • machine learning