The allotropy of titanium has been exploited by the researchers resulting in commercial titanium based alloys produced as α-titanium alloys, near-α-titanium alloys (small amount of β-phase), (α+β)-titanium and near-β-titanium alloys (small amount of α-phase), while β-titanium alloys have not been commercially produced in significant amounts. Other alloys such as those based on nickel, aluminum, and magnesium have single crystal structures at room temperature. Īt room temperature, titanium alloys can have two allotropes: hexagonal close-packed (HCP) known as α-Ti and body centered-cubic (BCC), also known as β-Ti. This further adds to the final cost of the titanium alloy parts. When using it as an aerospace component, it requires a specialized material removal process such as laser-assisted machining to fabricate and shape aircraft components free of machine damage. Additionally, extraction of titanium is an expensive process. One of the disadvantages of titanium alloys is their relatively high density (4.5 g/cm 3) when compared to aluminum alloys (2.7 g/cm 3) and carbon–epoxy composites (1.5–2.0 g/cm 3). Currently ~80 % of the global titanium production is consumed by the aerospace industry, and it is used more in military aircraft when compared with commercial aircraft.

As a result, titanium alloys are considered an important structural material in the aerospace industry. Titanium alloys possess high strength, fracture toughness, creep strength, excellent resistance to corrosion, and a longer fatigue life at a moderate density when compared to steel and nickel base alloys. This work should help alloy designers to determine the required chemical composition of a new alloy with reference to the temperature of application of that alloy and see the effect of temperature and composition on stable phases and associated properties of alloys. The concept of Self-Organizing Maps (SOM) was then applied to determine correlations between alloy compositions, stabilities of desired phases at various temperatures, associated Young’s moduli and densities, and the effect of the composition of phases on these properties. This resulted in a large dataset where a lot of data were still missing as all the phases are not stable at a given temperature for all the compositions. Data generated for the Ti–Al–Cr–V system of metallic alloys from our previous publication, where the composition of 102 alloys were computationally Pareto optimized with the objective of simultaneously maximizing the Young’s modulus and minimizing density for a range of temperatures, was the starting point of the current research, where compositions at different temperatures of these alloys were analyzed for phase stability in order to generate new data for compositions and volume fractions of stable phases at various temperatures.

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