Results 121 to 130 of about 85,322 (257)

Fatigue Crack Initiation and Growth in Nanocrystalline Ni at Multiple Length‐Scales

Advanced Engineering Materials, EarlyView.
Overview of miniaturized in situ SEM fatigue setup and resultant fatigue crack growth data for nanocrystalline Ni. The presented study focuses on the analysis of fatigue crack growth rate (FCGR) in focused ion beam‐notched microcantilevers prepared from nanocrystalline (NC) Ni as a model material.
Igor Moravcik, Alexander Jelinek, Markus Alfreider, Jutta Luksch, Anton Hohenwarter, Florian Schaefer, Christian Motz, Daniel Kiener   +7 more
wiley   +1 more source

Autonomous Uncertainty Quantification for Computational Point-of-Care Sensors. [PDF]

ACS Nano
Goncharov A, Ghosh R, Joung HA, Di Carlo D, Ozcan A.   +4 more
europepmc   +1 more source

Influence of Test Temperature and Test Frequency on Fatigue Life of Aluminum Alloy EN AW‐2618A

Advanced Engineering Materials, EarlyView.
The influence of test temperature and test frequency on the fatigue life of EN AW‐2618A is investigated. High‐cycle fatigue tests are performed at different test temperatures and frequencies on the 1000 h/230°C overaged state. Both test parameters reduce fatigue life due to time‐dependent damage mechanisms.
Ying Han, Julian M. Rosalie, Jan Radners, Mauro Madia, Philipp von Hartrott, Birgit Skrotzki   +5 more
wiley   +1 more source

Uncertainty Quantification in Inverse Scattering Problems. [PDF]

Entropy (Basel)
Abugattas C, Carpio A, Cebrián E.
europepmc   +1 more source

High‐Temperature Nanoindentation of Metals: Assessing Thermal Drift, Frame Compliance, and Chemical Composition Effects on the Reported Mechanical Properties

Advanced Engineering Materials, EarlyView.
Do not let thermal drift and instrument artifacts deceive high‐temperature nanoindentation results. We compare classical Oliver–Pharr and automatic image recognition analyses across steels and a Ni alloy to quantify these effects. Accounting for artifacts reveals systematic softening with temperature, while Cr and Ni additions boost resistance ...
Velislava Yonkova, Steffen Brinckmann, Ruth Schwaiger   +2 more
wiley   +1 more source

Uncertainty quantification of U-Net based segmentation tool using conformal prediction. [PDF]

J Appl Clin Med Phys
Borden BJ, Graham-Knight JB, Lasserre P, Lucas S, Rajapakshe RD.   +4 more
europepmc   +1 more source

Optimization of the Production of Rubber Compounds Using Mathematical Models

Advanced Engineering Materials, EarlyView.
Rubber compounds were mixed in a batch internal mixer, and symbolic regression was used to derive mathematical models linking recipe and process parameters to ram path, torque, and mixing quality (incorporation, dispersion, distribution). Subsequent optimization with evolutionary algorithms identified operating conditions that reduce specific energy ...
Anke Bardehle, Andreas Limper, Ulrich Giese, Carlos Lucke, Mario Beiner, Christoph Gögelein, Lisa Leuchtenberger‐Engel, Christian Hopmann   +7 more
wiley   +1 more source

Uncertainty Quantification for <i>In Silico</i> Chemistry. [PDF]

Chem Rev
Frömbgen T, Surzhikova E, Dölz J, Proppe J, Kirchner B, Jacob CR.   +5 more
europepmc   +1 more source

Ontology‐Aligned Structuring and Reuse of Multimodal Materials Data and Workflows Toward Automatic Reproduction

Advanced Engineering Materials, EarlyView.
Reproduction of stacking fault energy calculations from literature with a semi‐automated large language model‐assisted extraction procedure: extraction of simulation protocol, atomistic structures, computational parameters, and reported results, ontology alignment, knowledge graph construction and, finally, recomputation forvalidation.
Sepideh Baghaee Ravari, Abril Azocar Guzman, Sarath Menon, Stefan Sandfeld, Tilmann Hickel, Markus Stricker   +5 more
wiley   +1 more source

Microstructure Reconstruction in Battery Electrodes Using Machine Learning Based on Low‐Voltage Focused Ion Beam–Scanning Electron Microscopy Tomography Images

Advanced Engineering Materials, EarlyView.
Low‐voltage FIB‐SEM tomography combined with a image preprocessing pipeline improves phase contrast and enables reliable machine‐learning segmentation of conductive networks in lithium‐ion battery electrodes. Structural descriptors are extracted from segmented images, done semimanually and automated, and compared.
Lisa Beran, Vincent Nebel, Dominik Perius, Kshitij Kumar, Yannis Heim, Laura Bies, Tobias Kraus   +6 more
wiley   +1 more source