Experiments augmented computational analysis of structural materials: A focus on metallic and biological systems

Over the past few decades, the demand for energy-efficient treatment processes to reduce carbon emissions and the need for high performance materials in advanced engineering applications have posed significant challenges for materials scientists. This research first investigates the influence of high magnetic fields during heat treatment an energy efficient alternative to conventional processes on the microstructural evolution and mechanical properties of hypoeutectoid steels. The study demonstrates how magnetic fields affect phase transformations, microstructural features, and mechanical behavior. To establish a robust structure-property relationship and enable microstructural tailoring for targeted mechanical properties, an end-to-end computational framework integrating experimental characterization, physics based finite element simulations, and deep learning techniques is developed. Additionally, a mesoscale finite element model is constructed for fully pearlitic steels to simulate plastic deformation and damage, calibrated and validated using experimental data. A deep learning-based approach is then applied to analyze the structure-property relationships and design pearlite lamellae for optimized mechanical performance. Furthermore, the study extends to bio-inspired materials, investigating Nacre like structures for topology optimization aimed at enhancing mechanical properties and wave filtering capabilities. The dynamic behavior of these metamaterials is examined, revealing how hierarchical design influences their multifunctional properties. The findings of this research contribute to advancing the understanding of magnetic field assisted heat treatment for ferrous alloys, providing a computational framework for mesoscale plastic deformation and damage modeling in metallic systems, and developing methodologies for forward and inverse structural design targeting specific engineering applications. These insights pave the way for optimizing materials to achieve superior performance while promoting sustainable and efficient manufacturing processes.

Name thesis:degree_name

Doctor of Philosophy

Level thesis:degree_level

doctoral

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

• Bollineni, Ravi Kiran

Chair dc:contributor.committeechair

• Li, Ling

Committee members dc:contributor.committeemember

• Acar, Pinar
• Shahab, Shima
• Ahmadian, Mehdi
• Kesler, Michael

• Hypoeutectoid steels
• Pearlite steels
• Nacre structure
• Computational modeling
• Deep learning models
• Genetic optimization
• Bayesian optimization
• Microstructure inverse design

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:42639

OAI identifier oai:identifier

oai:vtechworks.lib.vt.edu:10919/124861