Investigation of the nano-mechanical properties of pulse electric sintered TiAl-based high entropy alloys by CALPHAD-based simulation and experimental studies

Abstract

In this study, four optimized septenary high entropy alloys (HEAs): Ti_14.286Al_14.286Cr_14.286Nb_14.286Ni_14.286Cu_14.286Co_14.286 (A), Ti₂₀Al₂₀Cr₅Nb₅Ni₁₉Cu₁₂Co₁₉ (B), Ti₂₀Al₂₀Cr₅Nb₅Ni₁₈Cu₁₄Co₁₈ (C) and Ti₂₀Al₂₀Cr₅Nb₅Ni₁₇Cu₁₆Co₁₇ (D) were designed theoretically by thermo-physical calculations and CALPHAD-based tool (ThermoCalc) to predict the phase diagram, stable phases formed, thermodynamic and mechanical properties of the HEAs prior to the experimentation process. The elemental feedstocks for the HEAs were mechanically alloyed at 10 hrs milling time in a wet environment before being consolidated via pulse electric sintering technique at a sintering temperature of 900 °C, heating rate of 100 °C/min, pressure of 50 MPa, and a dwelling time of 10 min. Nanoindentation testing was conducted to evaluate the nano-mechanical characteristics of the fabricated HEAs. 5 stable phases were identified- BCC_B2, FCC_L1₂, Sigma, Heusler and C15_Laves at varying fractions across all four HEAs. Simulation from the Property Model Calculator (PMC) module of the ThermoCalc software indicated intrinsic hardness values of 126.116 HV, 144.096 HV, 138.283 HV and 132.972 HV for alloys A, B, C and D respectively. Under 100 mN load, with a loading and unloading rate of 600 mN/min and a holding period of 2 secs, the nanoindentation results revealed that alloy B exhibited the highest nanohardness (15.185 GPa), the least penetration depth (427.822 nm) and highest elastic modulus (246.92 GPa) among the properties. Notably, increasing the composition of Cu at the expense of Ni and Co led to a BCC-FCC phase transformation, resulting in a significant decrease in nanohardness from alloy B to D. A comparative analysis of the hardness results simulated from the PMC module and the experimental nano-hardness results obtained exhibited a consistent trend, confirming the reliability of the predictive model.