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Metals
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Fatigue and Fracture of Advanced Metallic Materials
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Fatigue and Fracture of Advanced Metallic Materials

A Special Issue of Metals (ISSN 2075-4701) belonging to the section "Metal Failure Analysis".

Deadline for manuscript submissions: 15 November 2026 | Viewed by 1833

Editor

Dr. Mengyu Chai

E-Mail Website
Guest Editor
School of Chemical Engineering and Technology, Xi’an Jiaotong University, Xi’an 710049, China
Interests: acoustic emission; machine learning; fatigue; crack growth; fracture; mechanical properties

Special Issue Information

Dear Colleagues, 

Fatigue and fracture remain among the most critical failure modes affecting the reliability and service life of metallic structural components. A comprehensive understanding of the entire damage evolution process—from small fatigue crack initiation and growth to macroscopic crack propagation and final unstable fracture—is essential for accurate fatigue life prediction and damage tolerance assessment. This necessitates systematic investigation into the fatigue and fracture behaviors of metallic materials, as well as the underlying deformation and damage mechanisms.

Recent advancements in characterization techniques and in situ monitoring methods have significantly enhanced our ability to probe material degradation and visualize damage progression under cyclic loading. High-resolution electron microscopy, digital image correlation, and acoustic emission, among others, now enable real-time tracking of fatigue damage with unprecedented sensitivity and temporal resolution. Furthermore, the integration of multi-scale experimental observations with physically based models is opening new avenues for understanding the complex interplay between microstructure, loading conditions, and damage evolution.

This Special Issue, “Fatigue and Fracture of Advanced Metallic Materials”, aims to showcase cutting-edge research on the mechanical behavior, damage mechanisms, and advanced characterization of metallic materials under monotonic and cyclic loading. We invite contributions covering topics including, but not limited to, low- and high-cycle fatigue, fatigue crack initiation and propagation, small fatigue cracks, tensile and fracture behavior, microstructural effects on damage evolution, and emerging in situ characterization techniques. Both original research articles and comprehensive reviews are welcome, with the goal of advancing the fundamental understanding and engineering practice in the fatigue and fracture of metallic materials.

Dr. Mengyu Chai
Guest Editor

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 250 words) can be sent to the Editorial Office for assessment.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-anonymized peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Metals is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • fatigue
  • fracture
  • crack growth
  • metals
  • small fatigue cracks
  • microstructure
  • material characterization
  • online monitoring
  • life prediction

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Published Papers (2 papers)

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Research

34 pages, 8240 KB  
Article
Comparison of Performances of Machine Learning and Deep Learning Models for Prediction of Creep Rupture Life
by Muhammad Bilal Jan, Zengchao Wu and Mengyu Chai
Metals 2026, 16(7), 795; https://doi.org/10.3390/met16070795 - 14 Jul 2026
Viewed by 798
Abstract
Accurate prediction of creep rupture life is essential for ensuring the long-term reliability of high-temperature components in power generation and petrochemical industries. Selecting appropriate data-driven models for limited and heterogeneous creep datasets remains a critical challenge, as conventional accuracy-based comparisons do not fully [...] Read more.
Accurate prediction of creep rupture life is essential for ensuring the long-term reliability of high-temperature components in power generation and petrochemical industries. Selecting appropriate data-driven models for limited and heterogeneous creep datasets remains a critical challenge, as conventional accuracy-based comparisons do not fully capture model behavior under varying service conditions. This study presents a unified evaluation framework for systematically comparing multiple machine learning and deep learning models for creep rupture life prediction of 2.25Cr–1Mo steel. The framework integrates predictive accuracy, prediction reliability, regime-specific error analysis, and computational efficiency, enabling a comprehensive assessment beyond global error metrics. The input feature space is reduced from seventeen to eight physically meaningful variables without loss of predictive performance. To further assess model robustness, prediction errors are analyzed across four distinct rupture life regimes, revealing significant variations in model behavior that are not reflected in aggregate metrics. Results indicate that support vector regression (SVR) provides the most consistent overall performance across all regimes and offers a strong balance between accuracy and computational efficiency. Among deep learning models, a Bayesian neural network (BNN) achieves competitive predictive performance while additionally enabling uncertainty estimation. These findings demonstrate that, for small tabular creep datasets, appropriately regularized models outperform complex neural network architectures, highlighting the importance of matching model complexity to dataset characteristics. This study is limited to a single steel grade, moderate dataset size, and extrapolation beyond trained stress and temperature ranges, which are key directions for future work. Full article
(This article belongs to the Special Issue Fatigue and Fracture of Advanced Metallic Materials)
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16 pages, 2834 KB  
Article
A Fatigue Life Prediction Model of Aluminum Alloy Considering Crack Initiation
by Kaiyu Cheng, Shihao Ma, Yuanyuan Fang, Wei Guo, Xia Xu, Guoqiang Chang and Henggao Xiang
Metals 2026, 16(5), 530; https://doi.org/10.3390/met16050530 - 13 May 2026
Cited by 1 | Viewed by 602
Abstract
In high-cycle fatigue, the majority of fatigue life is spent in the crack initiation stage. However, current models fail to accurately capture the fatigue life consumed in the crack initiation stage, resulting in discrepancies in predictions. Here, we propose a fatigue life prediction [...] Read more.
In high-cycle fatigue, the majority of fatigue life is spent in the crack initiation stage. However, current models fail to accurately capture the fatigue life consumed in the crack initiation stage, resulting in discrepancies in predictions. Here, we propose a fatigue life prediction model based on the crack tip plastic zone, combined with a multi-stage crack growth approach. To quantify the crack initiation life, a modified Tanaka–Mura model is developed by incorporating the effects of localized plastic deformation at the crack tip. The proposed model demonstrates good agreement with experimental observations. Furthermore, a reliability-based fatigue evaluation framework is established by introducing a fatigue safety factor formulation. The results show that the safety factor decreases with increasing applied stress levels, attributed to the reduced standard deviation and lower scatter of fatigue life at higher stresses. The findings provide a practical and physics-informed methodology for fatigue life and safety assessment of aluminum alloy components under complex cyclic loading conditions. Full article
(This article belongs to the Special Issue Fatigue and Fracture of Advanced Metallic Materials)
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