Slip-trace-induced vicinal step destabilization

2016 ◽  
Vol 93 (4) ◽  
Author(s):  
C. Coupeau ◽  
O. Camara ◽  
M. Drouet ◽  
J. Durinck ◽  
J. Bonneville ◽  
...  
Keyword(s):  
Materials ◽  
2020 ◽  
Vol 13 (15) ◽  
pp. 3298
Author(s):  
Akhil Thomas ◽  
Ali Riza Durmaz ◽  
Thomas Straub ◽  
Chris Eberl

The digitization of materials is the prerequisite for accelerating product development. However, technologically, this is only beneficial when reliability is maintained. This requires comprehension of the microstructure-driven fatigue damage mechanisms across scales. A substantial fraction of the lifetime for high performance materials is attributed to surface damage accumulation at the microstructural scale (e.g., extrusions and micro crack formation). Although, its modeling is impeded by a lack of comprehensive understanding of the related mechanisms. This makes statistical validation at the same scale by micromechanical experimentation a fundamental requirement. Hence, a large quantity of processed experimental data, which can only be acquired by automated experiments and data analyses, is crucial. Surface damage evolution is often accessed by imaging and subsequent image post-processing. In this work, we evaluated deep learning (DL) methodologies for semantic segmentation and different image processing approaches for quantitative slip trace characterization. Due to limited annotated data, a U-Net architecture was utilized. Three data sets of damage locations observed in scanning electron microscope (SEM) images of ferritic steel, martensitic steel, and copper specimens were prepared. In order to allow the developed models to cope with material-specific damage morphology and imaging-induced variance, a customized augmentation pipeline for the input images was developed. Material domain generalizability of ferritic steel and conjunct material trained models were tested successfully. Multiple image processing routines to detect slip trace orientation (STO) from the DL segmented extrusion areas were implemented and assessed. In conclusion, generalization to multiple materials has been achieved for the DL methodology, suggesting that extending it well beyond fatigue damage is feasible.


2018 ◽  
Vol 145 ◽  
pp. 264-277 ◽  
Author(s):  
C.M. Cepeda-Jiménez ◽  
C. Prado-Martínez ◽  
M.T. Pérez-Prado

2019 ◽  
Vol 466 ◽  
pp. 454-458
Author(s):  
Benjamin Douat ◽  
Jérôme Colin ◽  
Roberto Bergamaschini ◽  
Francesco Montalenti ◽  
Michel Drouet ◽  
...  
Keyword(s):  

2017 ◽  
Vol 125 ◽  
pp. 431-441 ◽  
Author(s):  
Javier Varillas ◽  
Jan Očenášek ◽  
Jordi Torner ◽  
Jorge Alcalá

2020 ◽  
Vol 185 ◽  
pp. 1-12 ◽  
Author(s):  
Hojun Lim ◽  
Jay D. Carroll ◽  
Joseph R. Michael ◽  
Corbett C. Battaile ◽  
Shuh Rong Chen ◽  
...  

Author(s):  
Jun Zhao ◽  
Bin Jiang ◽  
Yuan Yuan ◽  
Qinghang Wang ◽  
Ming Yuan ◽  
...  
Keyword(s):  

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