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Advances in Modeling Fatigue Damage and Fracture of Engineering Materials

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

Deadline for manuscript submissions: closed (31 August 2023) | Viewed by 1502

Special Issue Editors


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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
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Co-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,

The investigation of the fatigue of engineering materials started more than one hundred years ago; however, with the development of new engineering materials, testing methods, and computational techniques, fatigue assessment was reinvented. Real-time imagery of fatigue damage—coupled with state-of-the-art sensor technology—made understanding damage mechanisms at a sub-microscale possible. Further insights into design against fatigue are achieved by the integration of computational methods in the fatigue investigation techniques that are continuously enhanced by ever-increasing computational power. Data-driven algorithms achieved complicated fatigue-related structure–property relationships that are computationally prohibitive when using physics-based modeling alone. The fatigue community is currently thirsty for interdisciplinary approaches to fatigue analysis, even after more than one hundred years of fatigue research. We kindly invite renowned and early fatigue researchers to contribute to this effort of leveling up current advances in modeling fatigue damage and fracture within the scope highlighted here.

Prof. Dr. Frank Walther
Dr. Mustafa Awd
Guest Editors

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Keywords

  • fatigue
  • damage
  • sensor technology
  • microscale damage
  • computational methods
  • data-driven algorithms
  • structure–property
  • effective mechanisms
  • physics-based modeling

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Published Papers (2 papers)

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Research

22 pages, 13530 KiB  
Article
Comparison of Various Intrinsic Defect Criteria to Plot Kitagawa–Takahashi Diagrams in Additively Manufactured AlSi10Mg
by Mohammed Intishar Nur, Meetkumar Soni, Mustafa Awd and Frank Walther
Materials 2023, 16(18), 6334; https://doi.org/10.3390/ma16186334 - 21 Sep 2023
Cited by 1 | Viewed by 1624
Abstract
Selective laser melting is a form of additive manufacturing in which a high-power density laser is used to melt and fuse metallic powders to form the final specimen. By performing fatigue and tensile tests under various loading conditions, the study sought to establish [...] Read more.
Selective laser melting is a form of additive manufacturing in which a high-power density laser is used to melt and fuse metallic powders to form the final specimen. By performing fatigue and tensile tests under various loading conditions, the study sought to establish the impact of internal defects on the specimens’ fatigue life. Scanning electron microscopy and finite element simulation were conducted to determine the defect characteristics and the stress intensity factor of the specimens. Four different methods were used to determine the intrinsic defect length of the specimen, using data such as grain size, yield strength, and hardness value, among others. Kitagawa–Takahashi and El-Haddad diagrams were developed using the results. A correction factor hypothesis was established based on the deviation of measured data. Using Paris law, fatigue life was determined and compared to the experimental results later. The study aims to select one or more approaches that resemble experimental values and comprehend how internal defects and loading situations affect fatigue life. This study’s findings shed light on how internal defects affect the fatigue life of selective laser-melted AlSi10Mg specimens and can aid in improving the fatigue life prediction method of additively manufactured components, provided an appropriate intrinsic crack criterion is selected. Full article
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30 pages, 13552 KiB  
Article
Fatigue Life Modelling of Steel Suspension Coil Springs Based on Wavelet Vibration Features Using Neuro-Fuzzy Methods
by C. H. Chin, S. Abdullah, S. S. K. Singh, A. K. Ariffin and D. Schramm
Materials 2023, 16(6), 2494; https://doi.org/10.3390/ma16062494 - 21 Mar 2023
Cited by 2 | Viewed by 1460
Abstract
This study proposed wavelet-based approaches to characterise random vibration road excitations for durability prediction of coil springs. Conventional strain-life approaches require long computational time, while the accuracy of the vibration fatigue methods is unsatisfactory. It is therefore a necessity to establish an accurate [...] Read more.
This study proposed wavelet-based approaches to characterise random vibration road excitations for durability prediction of coil springs. Conventional strain-life approaches require long computational time, while the accuracy of the vibration fatigue methods is unsatisfactory. It is therefore a necessity to establish an accurate fatigue life prediction model based on vibrational features. Wavelet-based methods were applied to determine the low-frequency energy and multifractality of road excitations. Strain-life models were applied for fatigue life evaluation from strain histories. ANFIS modelling was subsequently adopted to associate the vibration features with the fatigue life of coil springs. Results showed that the proposed wavelet-based methods were effective to determine the signal energy and multifractality of vibration signals. The established vibration-based models showed good fatigue life conservativity with a data survivability of more than 90%. The highest Pearson coefficient of 0.955 associated with the lowest RMSE of 0.660 was obtained by the Morrow-based model. It is suggested that the low-frequency energy and multifractality of the vibration signals can be used as fatigue-related features in life predictions of coil springs under random loading. Finally, the proposed model is an acceptable fatigue life prediction method based on vibration features, and it can reduce the dependency on strain data measurement. Full article
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