Cover
Vol. 25 No. 2 (2025)

Published: December 31, 2025

Pages: 129-137

Review Article

Evaluation of Residual Stresses and Retained Austenite in AISI 4330 Low-Alloy Steel: A Critical Review of Experimental and Numerical Simulation Methods

Zahraa Mohammed Fadhil 1 Haider Maath Mohammed 2
1 Department of Mechanical Engineering, College of Engineering, University of Basrah, Basrah, Iraq
2 Department of Material Engineering, College of Engineering, University of Basrah, Basrah, Iraq
History
  • Received: October 20, 2025
  • Revised: December 5, 2025
  • Accepted: December 25, 2025
  • Available Online: December 31, 2025
DOI

https://doi.org/10.33971/bjes.25.2.15

Abstract

AISI 4330 Low-alloy steel is good material for advanced application because of its properties including strength and longevity. However, performance may be modified with heat treatment procedures, include quenching and tempering. These processes can create residual stresses and retained austenite (RA), which have an effect on the metal's application. these factors influence fatigue life, dimensional stability, and fracture toughness of engineered components. uncontrolled residual stresses can reduce fatigue strength by up to 30%, while optimal retained austenite content (e.g., 5-10%) can enhance damage tolerance. This study focuses on residual stresses and retained austenite measurement in AISI 4330 low-alloy steel after heat treatment. including experimental and simulation methods. The review summarizes many scientific studies published between 2019 and 2024 and shows some main challenges. One challenge is the difference between experimental results (for example, from X-ray diffraction (XRD) and neutron (diffraction) and simulation results (especially using ANSYS software). Another challenge is that different methods for measuring retained austenite can give different results, which can change how we understand the steel's properties. The review also explains new progress in modeling heat treatment. This includes adding phase transformation models to finite element simulations. Future efforts should combine multiscale simulation, characterization, and machine learning to achieve predictive control over these properties in manufacturing.

Keywords

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