Open Access

ARTICLE

Investigation of TWIP/TRIP Effects in the CrCoNiFe System Using a High-Throughput CALPHAD Approach

Jize Zhang¹, T. P. C. Klaver², Songge Yang¹, Brajendra Mishra¹, Yu Zhong^1,*

1 Mechanical and Materials Engineering Department, Worcester Polytechnic Institute, Worcester, MA 01609, USA
2 IT4Innovations, VSB-Technical University of Ostrava, 17. listopadu 2172/15, Ostrava, 708 00, Czech Republic

* Corresponding Author: Yu Zhong. Email:

Computers, Materials & Continua 2025, 84(3), 4299-4311. https://doi.org/10.32604/cmc.2025.067793

Received 13 May 2025; Accepted 26 June 2025; Issue published 30 July 2025

Abstract

Designing high-performance high-entropy alloys (HEAs) with transformation-induced plasticity (TRIP) or twinning-induced plasticity (TWIP) effects requires precise control over stacking fault energy (SFE) and phase stability. However, the vast complexity of multicomponent systems poses a major challenge for identifying promising candidates through conventional experimental or computational methods. A high-throughput CALPHAD framework is developed to identify compositions with potential TWIP/TRIP behaviors in the Cr-Co-Ni and Cr-Co-Ni-Fe systems through systematic screening of stacking fault energy (SFE), FCC phase stability, and FCC-to-HCP transition temperatures (T₀). The approach combines TC-Python automation with parallel Gibbs energy calculations across hundreds of thousands of compositions, enabling efficient extraction of metastable FCC-dominant alloys. The high-throughput results find 214 compositions with desired properties from 160,000 candidates. Detailed analysis of the Gibbs energy distributions, phase fraction trends, and temperature-dependent SFE evolution reveals critical insights into the thermodynamic landscape governing plasticity mechanisms in HEAs. The results show that only a narrow region of the compositional space satisfies all screening criteria, emphasizing the necessity of an integrated approach. The screened compositions and trends provide a foundation for targeted experimental validation. Furthermore, this work demonstrates a scalable, composition-resolved strategy for predicting deformation mechanisms in multicomponent alloys and offers a blueprint for integrating thermodynamic screening with mechanistic understanding in HEA design.

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Keywords

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