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Advances in Fatigue Analysis and Numerical Simulation in Engineering Materials
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Advances in Fatigue Analysis and Numerical Simulation in Engineering Materials

A special issue of Materials (ISSN 1996-1944). This special issue belongs to the section "Mechanics of Materials".

Deadline for manuscript submissions: 10 June 2025 | Viewed by 2032

Special Issue Editors

Prof. Dr. Frank Walther

E-Mail Website
Guest Editor
Chair of Materials Test Engineering (WPT), TU Dortmund University, 44227 Dortmund, Germany
Interests: materials science and engineering; high-resolution microstructure and defect analysis; fatigue behavior with temperature and corrosion superposition; metrological material condition monitoring; fracture mechanics evaluation of damage tolerances; process-structure-property-damage interactions; mechanism-based material modeling and simulation
Special Issues, Collections and Topics in MDPI journals
Dr. Mustafa Awd

E-Mail Website
Guest Editor
Institute for Informatics and Automation, Bremen City University for Applied Sciences, D-28199 Bremen, Germany
Interests: artificial intelligence/machine learning; quantum mechanics/molecular dynamics; additive manufacturing (Ti, Al, and steels); numerical and statistical modeling; cyclic plasticity and fracture
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Over a century ago, research concerning the fatigue of engineered materials was initiated. However, the fatigue evaluation was reimagined with the advent of new engineering materials, testing protocols, and computer methodologies. Understanding damage mechanisms at the submicro scale was made feasible by the combination of state-of-the-art sensor technology and real-time images of fatigue damage. The incorporation of computational approaches to fatigue study procedures, which are continually improved by ever-increasing computer capacity, yields further insights into designs against fatigue. Complicated fatigue-related structure–property interactions that are computationally expensive when utilizing physics-based modeling alone were accomplished using data-driven algorithms. Even after extensive study, the fatigue community is now even more in need of multidisciplinary approaches to fatigue analysis. We cordially encourage distinguished and pioneering fatigue investigators to partake in this endeavor to elevate the present developments in fatigue damage and fracture modeling within the purview delineated below.

Prof. Dr. Frank Walther
Dr. Mustafa Awd
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 100 words) can be sent to the Editorial Office for announcement on this website.

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. Materials is an international peer-reviewed open access semimonthly 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
  • damage
  • sensor technology
  • microscale damage
  • computational methods
  • data-driven algorithms
  • structure–property interactions
  • effective mechanisms
  • physics-based modeling.

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Published Papers (2 papers)

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Research

22 pages, 3671 KiB  
Article
AI-Powered Very-High-Cycle Fatigue Control: Optimizing Microstructural Design for Selective Laser Melted Ti-6Al-4V
by Mustafa Awd and Frank Walther
Materials 2025, 18(7), 1472; https://doi.org/10.3390/ma18071472 - 26 Mar 2025
Viewed by 303
Abstract
Integrating machine learning into additive manufacturing offers transformative opportunities to optimize material properties and design high-performance, fatigue-resistant structures for critical applications in aerospace, biomedical, and structural engineering. This study explores mechanistic machine learning techniques to tailor microstructural features, leveraging data from ultrasonic fatigue [...] Read more.
Integrating machine learning into additive manufacturing offers transformative opportunities to optimize material properties and design high-performance, fatigue-resistant structures for critical applications in aerospace, biomedical, and structural engineering. This study explores mechanistic machine learning techniques to tailor microstructural features, leveraging data from ultrasonic fatigue tests where very high cycle fatigue properties were assessed up to 1×1010 cycles. Machine learning models predicted critical fatigue thresholds, optimized process parameters, and reduced design iteration cycles by over 50%, leading to faster production of safer, more durable components. By refining grain orientation and phase uniformity, fatigue crack propagation resistance improved by 20–30%, significantly enhancing fatigue life and reliability for mission-critical aerospace components, such as turbine blades and structural airframe parts, in an industry where failure is not an option. Additionally, the machine learning-driven design of metamaterials enabled structures with a 15% weight reduction and improved yield strength, demonstrating the feasibility of bioinspired geometries for lightweight applications in space exploration, medical implants, and high-performance automotive components. In the area of titanium and aluminum alloys, machine learning identified key process parameters such as temperature gradients and cooling rates, which govern microstructural evolution and enable fatigue-resistant designs tailored for high-stress environments in aircraft, biomedical prosthetics, and high-speed transportation. Combining theoretical insights and experimental validations, this research highlights the potential of machine learning to refine microstructural properties and establish intelligent, adaptive manufacturing systems, ensuring enhanced reliability, performance, and efficiency in cutting-edge engineering applications. Full article
(This article belongs to the Special Issue Advances in Fatigue Analysis and Numerical Simulation in Engineering Materials)
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31 pages, 13227 KiB  
Article
Notches and Fatigue on Aircraft-Grade Aluminium Alloys
by Valentin Zichil, Cosmin Constantin Grigoras and Vlad Andrei Ciubotariu
Materials 2024, 17(18), 4639; https://doi.org/10.3390/ma17184639 - 21 Sep 2024
Viewed by 1260
Abstract
The influence of notches and fatigue on the ultimate tensile strength and elongation at break of aluminium alloys (2024-T3, 6061-T4, 6061-T4 uncoated, 6061-T6 uncoated, 7075-T0, and 7076-T6) is presented in this study. A total of 120 specimens were used. On all specimens, notches [...] Read more.
The influence of notches and fatigue on the ultimate tensile strength and elongation at break of aluminium alloys (2024-T3, 6061-T4, 6061-T4 uncoated, 6061-T6 uncoated, 7075-T0, and 7076-T6) is presented in this study. A total of 120 specimens were used. On all specimens, notches were made using a CNC machine, with 60 of them subjected to low-cycle fatigue (LCF) before undergoing the tensile test. Based on the statistical examination of the measured data, mathematical prediction models have been established. Compared to their unscratched counterparts, the results indicate a significant decrease in the UTS and elongation at break for both notched and notched-fatigued specimens. The LCF pre-treatment contributes to the negative impacts of the notches, resulting in reduced values for the UTS and elongation at break, thus concluding that surface integrity is critical for maintaining the structural strength of aircraft components. Full article
(This article belongs to the Special Issue Advances in Fatigue Analysis and Numerical Simulation in Engineering Materials)
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