C
Chris Eberl
Researcher at University of Freiburg
Publications - 7
Citations - 117
Chris Eberl is an academic researcher from University of Freiburg. The author has contributed to research in topics: Deep learning & Metamaterial. The author has an hindex of 3, co-authored 7 publications receiving 28 citations. Previous affiliations of Chris Eberl include Fraunhofer Society.
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Journal ArticleDOI
Mechanical Metamaterials on the Way from Laboratory Scale to Industrial Applications: Challenges for Characterization and Scalability
TL;DR: An overview of the design space for metamaterials is provided, with focus on critical factors for scaling of manufacturing in order to fulfill industrial standards.
Journal ArticleDOI
Actuating Shape Memory Polymer for Thermoresponsive Soft Robotic Gripper and Programmable Materials.
Dennis Schönfeld,Dilip Chalissery,Franziska Wenz,Marius Specht,Marius Specht,Chris Eberl,Chris Eberl,Thorsten Pretsch +7 more
TL;DR: One actuator element each was built into two types of unit cells for programmable materials, thus enabling the design of temperature-dependent behavior, and is expected to open up new opportunities in the fields of soft robotics and shape morphing.
Posted ContentDOI
A deep learning approach for complex microstructure inference.
Ali Riza Durmaz,Ali Riza Durmaz,Ali Riza Durmaz,Martin Müller,Bo Lei,Akhil Thomas,Akhil Thomas,Dominik Britz,Elizabeth A. Holm,Chris Eberl,Chris Eberl,Frank Mücklich,Peter Gumbsch,Peter Gumbsch +13 more
TL;DR: Concepts about required data amounts and interpretability are resolved to pave the way for DL's day-to-day application for microstructure quantification.
Journal ArticleDOI
Automated Quantitative Analyses of Fatigue-Induced Surface Damage by Deep Learning
Akhil Thomas,Ali Riza Durmaz,Ali Riza Durmaz,Ali Riza Durmaz,Thomas Straub,Thomas Straub,Chris Eberl,Chris Eberl +7 more
TL;DR: Generalization to multiple materials has been achieved for the DL methodology, suggesting that extending it well beyond fatigue damage is feasible.
Journal ArticleDOI
Efficient Experimental and Data-Centered Workflow for Microstructure-Based Fatigue Data: Towards a Data Basis for Predictive AI Models
Ali Riza Durmaz,Ali Riza Durmaz,Ali Riza Durmaz,N. Hadzic,N. Hadzic,T. Straub,T. Straub,Chris Eberl,Chris Eberl,Peter Gumbsch,Peter Gumbsch +10 more
TL;DR: A combined experimental and data post-processing workflow to establish multimodal fatigue crack initiation and propagation data sets efficiently and lays the foundation for future data mining and data-driven modeling of microstructural fatigue by providing statistically meaningful data sets extendable to a wide range of materials.