Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
5.3
2.5
Journals
Metals
Special Issues
Fatigue and Fracture Mechanisms of Advanced Metallic Materials
share announcement

Fatigue and Fracture Mechanisms of Advanced Metallic Materials

A special issue of Metals (ISSN 2075-4701). This special issue belongs to the section "Metal Failure Analysis".

Deadline for manuscript submissions: 20 October 2026 | Viewed by 554

Special Issue Editors

Dr. Jianguang Bao

E-Mail Website
Guest Editor
Henry Royce Institute, Department of Materials, University of Manchester, Manchester M13 9PL, UK
Interests: synchrotron in situ experiments; additive manufacturing; damage and fracture
Dr. Zhengkai Wu

E-Mail Website
Guest Editor
Department of Mechanical Engineering, Kyushu University, Fukuoka 819-0395, Japan
Interests: multi-modal imaging; defects tolerence; additive manufacturing
Prof. Dr. Shengchuan Wu

E-Mail Website
Guest Editor
State Key Laboratory of Traction Power, Southwest Jiaotong University, Chengdu 610031, China
Interests: fatigue fracture; X-ray and neutron tomography; additive manufacturing
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Fatigue and fracture remain critical challenges in ensuring the long-term reliability and safety of advanced metallic materials. With the rapid development of additive manufacturing, welding technologies, and novel structural alloys, new opportunities and challenges have emerged in understanding their fatigue and fracture behavior. Moreover, the increasing demand for lightweight, high-strength, and durable materials in aerospace, transportation, and energy applications requires systematic studies that integrate both fundamental mechanisms and practical engineering solutions.

This Special Issue welcomes contributions addressing the processing–structure–property–performance relationships in advanced metallic materials under cyclic loading. Topics of interest include, but are not limited to, the following:

  • Fatigue and fracture mechanisms in additively manufactured and welded metallic structures.
  • In situ and advanced testing methodologies for characterizing fatigue crack initiation and growth.
  • The role of extreme environments (temperature, corrosive media, radiation, etc.) on fatigue resistance.
  • Application of machine learning and data-driven approaches in fatigue life prediction and damage assessment.
  • Microstructural design concepts, such as transformation-induced mechanisms and novel strengthening strategies, for fatigue performance enhancement.

This Special Issue aims to provide an interdisciplinary forum for advancing both experimental and computational approaches to fatigue and fracture in advanced metallic materials, fostering knowledge exchange and promoting innovative solutions for structural integrity in demanding applications.

Dr. Jianguang Bao
Dr. Zhengkai Wu
Prof. Dr. Shengchuan Wu
Guest Editors

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-blind 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 evaluation
  • additive manufacturing
  • structural materials
  • extreme environments
  • in situ testing
  • fracture characterization
  • machine learning
  • crack propagation

Benefits of Publishing in a Special Issue

  • Ease of navigation: Grouping papers by topic helps scholars navigate broad scope journals more efficiently.
  • Greater discoverability: Special Issues support the reach and impact of scientific research. Articles in Special Issues are more discoverable and cited more frequently.
  • Expansion of research network: Special Issues facilitate connections among authors, fostering scientific collaborations.
  • External promotion: Articles in Special Issues are often promoted through the journal's social media, increasing their visibility.
  • Reprint: MDPI Books provides the opportunity to republish successful Special Issues in book format, both online and in print.

Further information on MDPI's Special Issue policies can be found here.

Published Papers (1 paper)

Order results
Result details
Show export options expand_more Show export options expand_less
Select all
Export citation of selected articles as:

Review

45 pages, 6216 KB  
Review
Data-Driven and Hybrid Modeling for Metal Fatigue: A Review of Classical Methods, Machine Learning, and Physics-Informed Neural Networks
by Yuzhou Shi, Arko Suryadip Dey and Yazhou Qin
Metals 2026, 16(5), 476; https://doi.org/10.3390/met16050476 - 28 Apr 2026
Abstract
The prediction of metal fatigue life has evolved from classical empirical approaches to advanced, data-driven computational models. However, traditional methods struggle with large data scatter, complex variable-amplitude loading, and the cost of experimental testing. These limitations are particularly pronounced in additively manufactured (AM) [...] Read more.
The prediction of metal fatigue life has evolved from classical empirical approaches to advanced, data-driven computational models. However, traditional methods struggle with large data scatter, complex variable-amplitude loading, and the cost of experimental testing. These limitations are particularly pronounced in additively manufactured (AM) components, which exhibit random porosity and are highly sensitive to process parameters. This review integrates classical fatigue mechanics with modern data-driven methodologies. It evaluates fatigue-life prediction for metallic alloys, welded assemblies, and AM materials. We review classical prediction tools, machine learning (ML) algorithms, deep learning architectures, and physics-informed neural networks (PINNs). ML models capture nonlinear degradation patterns but suffer from limited interpretability (“black-box” behavior) and are unable to extrapolate from small datasets. Embedding governing physical laws into PINNs helps mitigate these limitations. This approach enhances physical consistency, reduces training-data requirements, and strengthens extrapolation capability. In additively manufactured metals, defect location is often a more critical predictor of fatigue failure than defect size or morphology. To address data scarcity, we highlight the use of generative adversarial networks and transfer learning. Integrated models, combined with real-time structural health monitoring data, enable accurate, dynamic digital twins and preemptive fatigue prognosis. Full article
(This article belongs to the Special Issue Fatigue and Fracture Mechanisms of Advanced Metallic Materials)
►▼ Show Figures

Graphical abstract

Show export options expand_more Show export options expand_less
Select all
Export citation of selected articles as:
 
Displaying articles 1-1
Back to TopTop