Thesis
Studying the mechanics of TiB via molecular dynamics and machine learning
University of Iowa
Master of Science (MS), University of Iowa
Autumn 2024
DOI: 10.25820/etd.007762
Abstract
This thesis investigates the mechanical properties of titanium boride (TiB) using a combined approach of molecular dynamics (MD) simulations and machine learning (ML) to address the growing demand for high-performance materials in industries like aerospace, automotive, and biomedical engineering. TiB, known for its exceptional hardness, stiffness, and thermal stability, is analyzed in various simulated conditions to bridge existing knowledge gaps regarding its mechanical response under different environmental and structural stressors.Molecular dynamics simulations are conducted to understand TiB’s behavior at an atomic level, focusing on the effects of temperature and defect variability on its mechanical properties, including Young’s modulus, tensile strength, and Poisson’s ratio. This study offers insights into TiB's elasticity and resilience under real-world conditions by creating simulations across a temperature range from 100 K to 2500 K and defect concentrations of 2% and 5%. The results demonstrate that while TiB exhibits high stiffness and strength, its performance is sensitive to thermal and structural changes, underscoring the importance of defect management and thermal considerations in practical applications.
A machine learning framework complements these MD simulations, enhancing predictive accuracy for TiB’s properties across conditions where data is limited or computationally intensive. Using deterministic and probabilistic models, the ML approach provides reliable predictions and confidence intervals for TiB’s mechanical properties, thereby supporting design decisions for applications requiring high material endurance. This integration of MD and ML contributes a robust dataset and modeling framework that offers significant utility for future research in materials science, advancing the design and optimization of TiB composites. Ultimately, this research presents a methodology that improves understanding of TiB’s mechanical behavior and establishes a foundation for further development of advanced TiB-based materials tailored to specific industry demands.
Details
- Title: Subtitle
- Studying the mechanics of TiB via molecular dynamics and machine learning
- Creators
- Akram Ghaffarigharehbagh
- Contributors
- Shaoping Xiao (Advisor)Caterina Lamuta (Committee Member)Kamran Samani (Committee Member)
- Resource Type
- Thesis
- Degree Awarded
- Master of Science (MS), University of Iowa
- Degree in
- Mechanical Engineering
- Date degree season
- Autumn 2024
- DOI
- 10.25820/etd.007762
- Publisher
- University of Iowa
- Number of pages
- xi, 96 pages
- Copyright
- Copyright 2024 Akram Ghaffarigharehbagh
- Language
- English
- Date submitted
- 12/09/2024
- Description illustrations
- color illustrations
- Description bibliographic
- Includes bibliographical references (pages 90-96).
- Public Abstract (ETD)
This research explores titanium boride (TiB), a promising material known for its strength, durability, and resistance to extreme temperatures. It is highly valuable for aerospace, automotive, and medical technology industries. By studying TiB at the atomic level, this work provides insights into how it behaves under different conditions such as varying temperatures and structural imperfections that it might face in real-world applications.
This study uses advanced computer simulations to investigate how TiB performs under stress, helping us understand its strengths and limitations. Machine learning models further enhance this research by predicting TiB’s performance in a range of scenarios, which could save time and resources compared to traditional experimental methods. This research not only deepens our understanding of TiB but also opens doors for designing even more durable materials. These findings could lead to stronger, longer-lasting components in cars, airplanes, and other high-demand environments, ultimately advancing the development of next-generation materials for modern technology.
- Academic Unit
- Mechanical Engineering
- Record Identifier
- 9984774867602771
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