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@PHDTHESIS{Govind:1019389,
author = {Govind, Kishan},
othercontributors = {Sandfeld, Stefan and Mayer, Joachim},
title = {{T}owards generalized machine learning models for
dislocation image analysis: a parametric based synthetic
data approach},
school = {Rheinisch-Westfälische Technische Hochschule Aachen},
type = {Dissertation},
address = {Aachen},
publisher = {RWTH Aachen University},
reportid = {RWTH-2025-08373},
pages = {1 Online-Ressource : Illustrationen},
year = {2025},
note = {Veröffentlicht auf dem Publikationsserver der RWTH Aachen
University 2026; Dissertation, Rheinisch-Westfälische
Technische Hochschule Aachen, 2025},
abstract = {Since the first observation of dislocations in the mid
1950s, when electron microscopy was used to visualize these
defects, there have been significant advancements in
microscopy techniques, allowing for the acquisition of
high-quality, high-resolution dislocation image data. Today,
it is even possible to perform in-situ mechanical testing,
enabling the observation of dislocation microstructure
evolution during the plastic deformation of materials. The
dislocation image data generated in such experiments need to
be studied quantitatively to facilitate meaningful
calculations and to understand the underlying mechanisms.
Deep learning methods, particularly image segmentation based
on convolutional neural networks like U-Net, offer a
powerful tool for segmenting dislocation lines which can
provide us a way to represent the dislocations as splines to
perform quantitative studies. However, these methods require
substantial amounts of labeled training data, requiring us
to perform many more experiments and labor-intensive manual
labeling of dislocation lines. Lack of high quality, large
quantity training data presents a significant challenge to
applying state-of-the-art deep learning models to
dislocation image data. This work addresses that challenge.
In this work, we introduce a novel parametric-based
synthetic data generation model, which enables the creation
of synthetic training datasets for deep learning-based
training of Transmission Electron Microscopy (TEM) images of
dislocation microstructures. The synthetic data generation
model proposed in this work is designed to generate training
data in a way that not only replicates the background of TEM
images but also renders complex dislocation
microstructures—an essential aspect of materials science
research. Two distinct methods are used for generating
synthetic image backgrounds. The first method leverages
Perlin noise, combined with random white noise, to create a
purely synthetic background, offering a controlled
environment for dislocation rendering. The second method,
which is much more realistic, uses patches of backgrounds
from real TEM images, reassembling them to form
realistic-looking backgrounds. This approach mirrors the
complexity and variability present in real TEM images,
providing a more accurate context for the synthetic
dislocation structures. The core innovation of this work
lies in the modeling of dislocation microstructures for
synthetic training data. We start with dislocation line and
model it as a spline by providing support points for the
spline. By representing dislocations as splines, the model
achieves high fidelity in simulating dislocation patterns,
such as dislocation pileups. These support points can be
obtained through two methods: polynomial approximation of
dislocation lines or manual selection of key points using
image annotation tools like Labelme on dislocations in real
TEM images. This flexibility allows for the creation of
diverse range of dislocation microstructures consisting of a
wide range of configurations, such as dislocation pileups,
with varying slip widths, directions, and dislocation
counts. Additionally, two more structures—slip trace lines
and grain boundaries—are incorporated into the
microstructure which are modelled as a line, further aiding
machine learning models in learning the characteristics of
dislocations and improving predictive accuracy. The ability
to generate complex dislocation structures, some of which
are challenging or even impossible to observe in actual TEM
images, is particularly significant. After generating the
synthetic training data, the next step involves training
machine learning models. In this work, we explore three
different machine learning approaches. The first two
approaches, multi-label segmentation and instance
segmentation, predict individual dislocations as binary
masks, which need to be post-processed to represent
dislocations as splines and obtain digital representation of
the image. Third approach is a more direct approach which
estimates the spline support points on the dislocations to
represent the dislocation splines directly. We conduct
extensive studies to demonstrate the use of the synthetic
data and show how it can be used as an alternate to real
experimental data or along with real data. This research
represents an important step toward developing generalized
machine learning models for dislocation analysis by
leveraging synthetic data. The development of a novel
parametric-based synthetic data generation model addresses
the need of obtaining high-quality training data for machine
learning models, particularly for TEM image analysis. The
synthetic data generation model enables the creation of
synthetic images that closely resemble real TEM images while
capturing complex dislocation structures. By generating
diverse and realistic training datasets, this research opens
up new possibilities for applying advanced deep learning
methods, such as U-Net and Mask R-CNN, to the segmentation
and analysis of dislocations enabling high throughout
studies. Furthermore, the study demonstrates the
effectiveness of using machine learning models trained on
synthetic data to perform quantitative analysis on real
experimental data, reinforcing the practical applicability
of these methods in material science research and offers
valuable insights into the mechanisms of plastic
deformation, further contributing to our understanding of
material behavior.},
cin = {527210 / 520000},
ddc = {620},
cid = {$I:(DE-82)527210_20201120$ / $I:(DE-82)520000_20140620$},
pnm = {MuDiLingo - A Multiscale Dislocation Language for
Data-Driven Materials Science (759419)},
pid = {G:(EU-Grant)759419},
typ = {PUB:(DE-HGF)11},
doi = {10.18154/RWTH-2025-08373},
url = {https://publications.rwth-aachen.de/record/1019389},
}
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