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

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 li...

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Autores principales: Thomas, Akhil, Durmaz, Ali Riza, Straub, Thomas, Eberl, Chris
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2020
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7435935/
https://www.ncbi.nlm.nih.gov/pubmed/32722181
http://dx.doi.org/10.3390/ma13153298
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author Thomas, Akhil
Durmaz, Ali Riza
Straub, Thomas
Eberl, Chris
author_facet Thomas, Akhil
Durmaz, Ali Riza
Straub, Thomas
Eberl, Chris
author_sort Thomas, Akhil
collection PubMed
description 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.
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spelling pubmed-74359352020-08-24 Automated Quantitative Analyses of Fatigue-Induced Surface Damage by Deep Learning Thomas, Akhil Durmaz, Ali Riza Straub, Thomas Eberl, Chris Materials (Basel) Article 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. MDPI 2020-07-24 /pmc/articles/PMC7435935/ /pubmed/32722181 http://dx.doi.org/10.3390/ma13153298 Text en © 2020 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
Thomas, Akhil
Durmaz, Ali Riza
Straub, Thomas
Eberl, Chris
Automated Quantitative Analyses of Fatigue-Induced Surface Damage by Deep Learning
title Automated Quantitative Analyses of Fatigue-Induced Surface Damage by Deep Learning
title_full Automated Quantitative Analyses of Fatigue-Induced Surface Damage by Deep Learning
title_fullStr Automated Quantitative Analyses of Fatigue-Induced Surface Damage by Deep Learning
title_full_unstemmed Automated Quantitative Analyses of Fatigue-Induced Surface Damage by Deep Learning
title_short Automated Quantitative Analyses of Fatigue-Induced Surface Damage by Deep Learning
title_sort automated quantitative analyses of fatigue-induced surface damage by deep learning
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7435935/
https://www.ncbi.nlm.nih.gov/pubmed/32722181
http://dx.doi.org/10.3390/ma13153298
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