Research Article Open Access

Analyzing Temperature-Dependent Thermal Properties of Titanium Aluminide Using ANN Predictive Modeling

Armaghan Shalbaftabar1, Kristen Rhinehardt1 and Dhananjay Kumar2
  • 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.

American Journal of Engineering and Applied Sciences
Volume 17 No. 4, 2024, 169-179

DOI: https://doi.org/10.3844/ajeassp.2024.169.179

Submitted On: 24 June 2024 Published On: 20 November 2024

How to Cite: Shalbaftabar, A., Rhinehardt, K. & Kumar, D. (2024). Analyzing Temperature-Dependent Thermal Properties of Titanium Aluminide Using ANN Predictive Modeling. American Journal of Engineering and Applied Sciences, 17(4), 169-179. https://doi.org/10.3844/ajeassp.2024.169.179

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Keywords

  • ANN
  • Titanium
  • Aluminum
  • Material Properties Prediction
  • Temperature Analysis