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Claim

Explainable machine learning for precise fatigue crack tip detection

integration_instructionsResearch softwarekeyboard_double_arrow_right Software 01 Jun 2022Publisher:Zenodo
Authors: Melching, David; Strohmann, Tobias; Requena, Guillermo; Breitbarth, Eric;

doi: 10.5281/zenodo.6605440 , 10.5281/zenodo.6602447 , 10.5281/zenodo.6602446 , 10.5281/zenodo.6605441

Explainable machine learning for precise fatigue crack tip detection

Abstract

The repository contains the code belonging to the research arcticle "Explainable machine learning for precise fatigue crack tip detection" (Sci Rep, 2022). Visit https://github.com/dlr-wf/explainable-crack-tip-detection for more information and the latest updates. Visit https://zenodo.org/record/5740216 for the dataset used in the article.

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selected citations
These citations are derived from selected sources.
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
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