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Efficient Experimental and Data-Centered Workflow for Microstructure-Based Fatigue Data – Towards a Data Basis for Predictive AI Models

Durmaz, A. R. 1; Hadzic, N.; Straub, T.; Eberl, C.; Gumbsch, P. 1
1 Karlsruher Institut für Technologie (KIT)

Abstract:

Background
Early fatigue mechanisms for various materials are yet to be unveiled for the (very) high-cycle fatigue (VHCF) regime. This can be ascribed to a lack of available data capturing initial fatigue damage evolution, which continues to adversely affect data scientists and computational modeling experts attempting to derive microstructural dependencies from small sample size data and incomplete feature representations.

Objective
The aim of this work is to address this lack and to drive the digital transformation of materials such that future virtual component design can be rendered more reliable and more efficient. Achieving this relies on fatigue models that comprehensively capture all relevant dependencies.

Methods
To this end, this work proposes a combined experimental and data post-processing workflow to establish multimodal fatigue crack initiation and propagation data sets efficiently. It evolves around fatigue testing of mesoscale specimens to increase damage detection sensitivity, data fusion through multimodal registration to address data heterogeneity, and image-based data-driven damage localization.

Results
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Verlagsausgabe §
DOI: 10.5445/IR/1000136394
Veröffentlicht am 16.08.2021
Originalveröffentlichung
DOI: 10.1007/s11340-021-00758-x
Scopus
Zitationen: 4
Web of Science
Zitationen: 4
Dimensions
Zitationen: 6
Cover der Publikation
Zugehörige Institution(en) am KIT Institut für Angewandte Materialien – Computational Materials Science (IAM-CMS)
Publikationstyp Zeitschriftenaufsatz
Publikationsmonat/-jahr 11.2021
Sprache Englisch
Identifikator ISSN: 0014-4851, 1741-2765
KITopen-ID: 1000136394
Erschienen in Experimental mechanics
Verlag Springer
Band 61
Heft 9
Seiten 1489–1502
Vorab online veröffentlicht am 02.08.2021
Schlagwörter Crack initiation, Crack propagation, Microstructure, Data fusion, Data-driven methods, Deep learning, Multimodal data registration
Nachgewiesen in Dimensions
Scopus
Web of Science
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