Design of High Temperature Ti–Al–Cr–V Alloys for Maximum Thermodynamic Stability Using Self-Organizing Maps

Abstract

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. 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. 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 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.

Discovery of New Ti-Based Alloys Aimed at Avoiding/Minimizing Formation of α” and ω-Phase Using CALPHAD and Artificial Intelligence

“…Metastable phase α" is denoted by ALTI3_D019, while metastable phase ω is denoted by OMEGA in TCTI2 database [37]. These notations were also mentioned in our previous publication on titanium-based alloys [28], which is featured on Thermo-Calc's website [38]. We have provided our previous publication on titanium alloys as a reference since the titanium database was recently launched by Thermo-Calc in 2018.…”

“…Data obtained from CALPHAD-based calculations and predicted through DLANN models were further studied by the concept of self-organizing maps (SOM) [25,28,31,42]. SOM maps are known for preserving topology of the data, which is helpful in determining various correlations in the dataset among concentrations of alloying element, temperature, and stability of various stable and metastable phases.…”

“…SOM analysis [25,28,31,42] was performed on the data obtained through the CAL-PHAD approach and DLANN models. From CALPHAD and DLANN analysis, we have a matrix of 3000 rows and nine columns.…”

“…Thus, we included all the design variables and the objectives. Calculations were performed in batch mode, where all the designs are introduced to the SOM algorithm with value of X unit set at 15 and Y unit assigned a value of 18 [28,31]. Thus, there are 270 map units on a SOM map.…”

“…Our research team has significant experience in designing alloys by application of artificial intelligence (AI) algorithms on data generated from experiments and data generated under the framework of the CALPHAD approach. We have successfully designed titanium alloys [28], aluminum alloys [29,30], hard magnets (AlNiCo) [31,32], soft magnets (FINEMET type) [33,34], and Ni-based superalloys [35]. Thus, we propose this computational design approach, which can be easily adopted in other alloy systems and can help in developing ω-phase free Ti-based biomaterials with improved mechanical properties.…”