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

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.