Analyzing Temperature-Dependent Thermal Properties of Titanium Aluminide Using ANN Predictive Modeling
- 1 Department of Computational Data Science and Engineering, North Carolina A&T State University, Greensboro, United States
- 2 Department of Mechanical Engineering, North Carolina A&T State University, Greensboro, United States
Abstract
This study presents a comprehensive analysis of the thermal behavior of Titanium Aluminide (TiAl) across a range of temperatures using an Artificial Neural Network (ANN) predictive model. The study investigates various material properties of TiAl, including Band Gap, Young Module, Density, Energy Absorption, Thermal Conductivity, and Specific Heat, at different temperature points. The ANN model accurately captures the temperature-dependent trends in TiAl's material properties, demonstrating consistent behavior as temperature varies. The findings contribute valuable insights into TiAl's thermal characteristics and have significant implications for its practical applications in industries such as pharmaceutical, automotive, and manufacturing. These insights can guide the development of more efficient and durable TiAl-based materials and components, enhancing their practical applications in demanding thermal conditions across industries that could lead to advancements in pharmaceutical equipment where temperature control is critical for processes like drug synthesis and sterilization, engine components, automotive exhaust systems, and high-temperature manufacturing equipment.
DOI: https://doi.org/10.3844/ajeassp.2024.169.179
Copyright: © 2024 Armaghan Shalbaftabar, Kristen Rhinehardt and Dhananjay Kumar. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
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Keywords
- ANN
- Titanium
- Aluminum
- Material Properties Prediction
- Temperature Analysis