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Residual Stress Relaxation in Engineering Materials: Influencing Factors, Mechanisms, and Machine Learning-Driven
Weichao Bao1, Hua Li2, Haikun Ma1
1Hebei Short Process Steelmaking Technology Innovation Center, School of Materials Science and Engineering, Hebei University of Science and Technology, Shijiazhuang, China.
Annals of the New York Academy of Sciences
|May 13, 2026
Summary
Residual stress relaxation (RSR) in mechanical components under cyclic loads and high temperatures can reduce fatigue life. This study reviews RSR factors, mechanisms, and proposes future machine learning research for better prediction.
Area of Science:
- Materials Science
- Mechanical Engineering
- Computational Mechanics
Background:
- Mechanical components face fatigue cracks due to alternating loads, leading to failure.
- Compressive residual stress enhances fatigue life by counteracting tensile stress.
- Residual stress relaxation (RSR) under mechanical loads and high temperatures compromises fatigue performance.
Purpose of the Study:
- To comprehensively review the influencing factors and intrinsic mechanisms of RSR.
- To discuss the challenges and future directions of machine learning in residual stress prediction.
Main Methods:
- Literature review of experimental and simulation studies on RSR.
- Analysis of RSR influencing factors: cyclic loading, temperature, work hardening, initial stress.
- Discussion of RSR mechanisms: dislocation annihilation, proliferation, rearrangement, and slip.
Main Results:
- Identified key factors influencing RSR, including cyclic loading, temperature, work hardening, and initial stress levels.
- Elucidated the fundamental mechanisms of RSR through dislocation dynamics.
- Highlighted the potential and challenges of machine learning for predicting residual stress.
Conclusions:
- RSR is a critical phenomenon affecting component fatigue life, influenced by multiple factors and driven by specific dislocation motions.
- Machine learning offers promising avenues for residual stress prediction, but addressing data quality and model interpretability is crucial for industrial application.
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