Data-Driven Characterization of Micro-structural Shape and Topology in Engineering Materials

This thesis presents a data-driven framework for the quantitative characterization of microstructural shape and topology in engineering materials, integrating invariant geometric descriptors and statistical dimensionality reduction techniques. Specifically, Hu moments, Principal Eigenvalue Moment (PEM), and Principal Component Analysis (PCA) are applied to a diverse dataset comprising experimental images of Titanium-Aluminum alloys and Inconel 718 superalloy, computationally designed meta-materials including unit cells and spinodoids, and synthetic microstructures generated via deep learning models such as Progressive Generative Adversarial Network (PGAN) and Denoising Diffusion Probabilistic Models (DDPM). Both Hu moments and PEM portray a high degree of invariance to rotation and scaling, showing considerable effectiveness in capturing morphologic features like grain size, ellipticity, and asymmetry. PCA provides a complementary perspective by revealing global variance patterns in pixel intensity distributions, although it is sensitive to rotation and color information. The result reflects that synthetic images generated from DDPM closely mimic real microstructure data in terms of both shape and texture, whereas images from the PGAN model align better with color-based PCA. The framework supports reproducible and scalable quantification of microstructures, which aids materials informatics, classification, and computational materials design.

Name thesis:degree_name

Master of Science

Level thesis:degree_level

masters

Discipline thesis:degree_discipline

Mechanical Engineering

Department dc:contributor.department

Mechanical Engineering

Grantor dc:publisher

Virginia Tech

Year dc:date.issued

2025

Author dc:creator

• Kuang, Kuang

Chair dc:contributor.committeechair

• Acar, Pinar

Committee members dc:contributor.committeemember

• Chen, Jie
• West, Robert L.

• Microstructure
• Image Processing
• Shape and Topology Quantification
• Engineering Materials

Statement dc:rights

• In Copyright

Licence dc:rights.uri

• http://rightsstatements.org/vocab/InC/1.0/

Language dc:language.iso

en

Dc Identifier Other

vt_gsexam:44020

OAI identifier oai:identifier

oai:vtechworks.lib.vt.edu:10919/134227