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Article

Automated Quantitative Analyses of Fatigue-Induced Surface Damage by Deep Learning

by 1,*,†, 1,2,3,*,†, 1,2 and 1,2
1
Fraunhofer Institute for Mechanics of Materials, 79108 Freiburg im Breisgau, Germany
2
Department of Microsystems Engineering, University of Freiburg, 79110 Freiburg, Germany
3
Institute for Applied Materials Computational Materials Science IAM-CMS, Karlsruhe Institute of Technology, 76131 Karlsruhe, Germany
*
Authors to whom correspondence should be addressed.
These authors contributed equally to this work.
Materials 2020, 13(15), 3298; https://doi.org/10.3390/ma13153298
Received: 17 June 2020 / Revised: 14 July 2020 / Accepted: 20 July 2020 / Published: 24 July 2020
(This article belongs to the Special Issue Micromechanics: Experiment, Modeling and Theory)
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. View Full-Text
Keywords: deep learning; semantic segmentation; extrusions; micro cracks; slip trace analysis; generalization deep learning; semantic segmentation; extrusions; micro cracks; slip trace analysis; generalization
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MDPI and ACS Style

Thomas, A.; Durmaz, A.R.; Straub, T.; Eberl, C. Automated Quantitative Analyses of Fatigue-Induced Surface Damage by Deep Learning. Materials 2020, 13, 3298. https://doi.org/10.3390/ma13153298

AMA Style

Thomas A, Durmaz AR, Straub T, Eberl C. Automated Quantitative Analyses of Fatigue-Induced Surface Damage by Deep Learning. Materials. 2020; 13(15):3298. https://doi.org/10.3390/ma13153298

Chicago/Turabian Style

Thomas, Akhil; Durmaz, Ali R.; Straub, Thomas; Eberl, Chris. 2020. "Automated Quantitative Analyses of Fatigue-Induced Surface Damage by Deep Learning" Materials 13, no. 15: 3298. https://doi.org/10.3390/ma13153298

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