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@PHDTHESIS{Medghalchi:989755,
author = {Medghalchi, Setareh},
othercontributors = {Korte-Kerzel, Sandra and Münstermann, Sebastian},
title = {{I}nvestigation of damage sites in dual phase
microstructures by artificial intelligence},
school = {Rheinisch-Westfälische Technische Hochschule Aachen},
type = {Dissertation},
address = {Aachen},
publisher = {RWTH Aachen University},
reportid = {RWTH-2024-06967},
pages = {1 Online-Ressource : Illustrationen},
year = {2024},
note = {Veröffentlicht auf dem Publikationsserver der RWTH Aachen
University; Dissertation, Rheinisch-Westfälische Technische
Hochschule Aachen, 2024, Kumulative Dissertation},
abstract = {The current thesis delves into the multifaceted analysis of
damage formation in advanced metallic materials with
heterogeneous microstructure under various loading
conditions, providing invaluable insights into damage
mechanisms, stress-state dependence, strain rate effects,
and three dimensional characterisation as well as phase
distribution. The studies encompass dual-phase steels and a
cast MgAlCa alloy, shedding light on diverse aspects of
damage behaviour and its implications for material
performance. Comprehensive high-resolution data acquisition
from relatively large experimental sample surfaces is
mandated to elicit statistically robust inferences, given
the profound influence of manufacturing processes on
microstructural attributes. Predominantly, the vast
heterogeneity within industrially manufactured sheets
remains elusive to traditional characterisation techniques,
which are mostly constrained to relatively diminutive
analysis scopes, thus undermining the representativeness of
empirical scrutiny. Recent advancements in deep learning,
particularly convolutional neural networks (CNNs), have
proven effective in analysing large datasets and extracting
visual features in materials science. Six research
publications are comprised in this thesis, in which the
image data of deformation induced damage sites, collected by
scanning electron microscope (SEM) across relatively large
areas, have been characterised and quantified from different
perspectives. Publication 1, explores the application of
deep learning in damage analysis for dual-phase steel (DP800
specifically), extending the methodology from uniaxial to
biaxial straining conditions. Leveraging data augmentation,
the research enhances network performance and investigates
changes in damage sites’ behaviour, particularly the
martensite crack angle distribution, in response to
different stress states. Facilitated by the enhanced method
in publication 1, the damage behaviour of DP800 steel is
characterised under single and combined strain paths,
revealing a strong connection between loading direction and
damage formation in publication 2. The study employs the
enhanced deep learning based method, for damage
quantification, finite element modelling for stress state
determination, and nanoindentation for strain hardening
assessment. Publication 3 focuses on a cast MgAlCa alloy and
explores the rate dependence of damage formation at elevated
temperatures. Utilising convolutional neural networks, the
research identifies changing dominant damage mechanisms with
varying strain rate. It demonstrates that the strain rate
influences the evolution of microcracks and interface
decohesion, shedding light on the influence of plastic
co-deformation of the α-Mg matrix and Laves phase as well
as thermally activated processes at the interface.
Publication 4 addresses the three dimensional
characterisation of the damage sites in dual phase steel,
presenting a comprehensive methodology that combines
metallographic serial sectioning, and deep learning assisted
automatic image analysis. This approach allows for the
analysis of thousands of individual damage sites in three
dimensions, offering a unique perspective on active damage
mechanisms during deformation. In publication 5, phase
segmentation of dual phase steel has been accomplished
through the implementation of two deep learning algorithms
developed in this work. They have facilitated the
calculation of precise martensite phase fraction with
statistical relevance, and the extraction of statistics
pertaining to the geometrical properties of damage sites and
the surrounding microstructure such as phase fraction. In
publication 6, a deep learning algorithm was trained using a
large set of segmented microstructural images, generated in
publication 5, and corresponding finite element simulation
results, demonstrate promising potential for predicting
mechanical stress and strain fields. This has the potential
to bridge computational simulations and experimental
materials science, expediting stress and strain analysis for
robust material design strategies. Collectively, these
studies provide a holistic understanding of damage formation
in two groups of advanced metallic materials, considering
factors such as stress state, strain path, 3D configuration
and local microstructural properties. Such insights are
invaluable for optimising material performance in real-world
applications, such as automotive engineering and beyond.},
cin = {523110 / 520000},
ddc = {620},
cid = {$I:(DE-82)523110_20140620$ / $I:(DE-82)520000_20140620$},
typ = {PUB:(DE-HGF)11},
doi = {10.18154/RWTH-2024-06967},
url = {https://publications.rwth-aachen.de/record/989755},
}
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