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Advances in Statistical Analysis of Fatigue Data
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Advances in Statistical Analysis of Fatigue Data

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

Deadline for manuscript submissions: closed (20 January 2024) | Viewed by 2190

Special Issue Editors

Dr. Andrea Tridello

E-Mail Website
Guest Editor
Department of Mechanical and Aerospace Engineering, Politecnico di Torino, 10129 Turin, Italy
Interests: high-cycle fatigue and very-high-cycle fatigue; probabilistic methods in fatigue and fracture; fatigue damage; structural integrity of additively manufactured and composite materials; failure analysis
Special Issues, Collections and Topics in MDPI journals
Dr. Davide S. Paolino

E-Mail Website
Guest Editor
Department of Mechanical and Aerospace Engineering, Politecnico di Torino, Corso Duca degli Abruzzi 24, 10129 Torino, Italy
Interests: statistical analysis of fatigue data; high-cycle fatigue and very-high-cycle fatigue; probabilistic fatigue and fracture; structural integrity of additive manufacturing materials and components
Special Issues, Collections and Topics in MDPI journals
Dr. Carlo Boursier Niutta

E-Mail Website
Guest Editor
Department of Mechanical and Aerospace Engineering, Politecnico di Torino, 10129 Turin, Italy
Interests: high-cycle and very high-cycle fatigue; structural integrity of composite and additively manufactured materials; size effect and fatigue failure; numerical modelling of damage and failure
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

The development of appropriate statistical methodologies for modelling the fatigue response of components is fundamental to ensure their structural integrity and guarantee safe design. Fatigue is known to be one of the primary causes of the failures of components and is affected by many factors, e.g., type of load, component size, manufacturing defects and type of microstructure, all of which significantly influence the in-service life of the component. These factors contribute to the large scatter shown by the fatigue test data. This intrinsic variability of the fatigue phenomenon cannot be disregarded and must be considered in a statistical framework. Statistical models have been proposed since the beginning of the research on the fatigue response of components. Nowadays, new challenges are to be faced by researchers working in this field, e.g., the modelling of an extended fatigue response from the Low Cycle Fatigue (LCF) to the Very High Cycle Fatigue (VHCF), the development of efficient testing methodologies and design procedures that limit the testing time, or the assessment of the stress–life relation in the presence of several failure modes.

This Special Issue aims at providing an overview of the recent advances in the statistical modelling of the fatigue response, a fundamental subject for the structural integrity of components prone to fatigue failure. Papers on innovative statistical models, the comparison between conventional and innovative methodologies, efficient methods limiting the testing time without affecting the design reliability, the estimation of the design curves (at different confidence and reliability levels) and literature reviews are welcome.

Dr. Andrea Tridello
Dr. Davide S. Paolino
Dr. Carlo Boursier Niutta
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 response
  • statistical modelling
  • fatigue life distribution
  • P-S-N curves
  • design curves
  • staircase method
  • fatigue limit

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Published Papers (2 papers)

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Research

16 pages, 3978 KiB  
Article
Merging Data with Modeling: An Example from Fatigue
by D. Gary Harlow
Materials 2024, 17(14), 3383; https://doi.org/10.3390/ma17143383 - 9 Jul 2024
Viewed by 521
Abstract
It is well known that errors are inevitable in experimental observations, but it is equally unavoidable to eliminate errors in modeling the process leading to the experimental observations. If estimation and prediction are to be done with reasonable accuracy, the accumulated errors must [...] Read more.
It is well known that errors are inevitable in experimental observations, but it is equally unavoidable to eliminate errors in modeling the process leading to the experimental observations. If estimation and prediction are to be done with reasonable accuracy, the accumulated errors must be adequately managed. Research in fatigue is challenging because modeling can be quite complex. Furthermore, experimentation is time-consuming, which frequently yields limited data. Both of these exacerbate the magnitude of the potential error. The purpose of this paper is to demonstrate a procedure that combines modeling with independent experimental data to improve the estimation of the cumulative distribution function (cdf) for fatigue life. Subsequently, the effect of intrinsic error will be minimized. Herein, a simplified fatigue crack growth modeling is used. The data considered are a well-known collection of fatigue lives for an aluminum alloy. For lower applied stresses, the fatigue lives can range over an order of magnitude and up to 107 cycles. For larger applied stresses, the scatter in the lives is considerably reduced. Consequently, modeling must encompass a variety of conditions. The primary conclusion of the effort is that merging independent experimental data with a reasonably acceptable model vastly improves the accuracy of the calibrated cdfs for fatigue life, given the loading conditions. This allows for improved life estimation and prediction. For the aluminum data, the calibrated cdfs are shown to be quite good by using statistical goodness-of-fit tests, stress-life (S-N) analysis, and confidence bounds estimated using the mean square error (MSE) method. A short investigation into the effect of sample size is also included. Thus, the proposed methodology is warranted. Full article
(This article belongs to the Special Issue Advances in Statistical Analysis of Fatigue Data)
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15 pages, 4445 KiB  
Article
Crankshaft High-Cycle Bending Fatigue Experiment Design Method Based on Unscented Kalman Filtering and the Theory of Crack Propagation
by Tianyi Que, Dongdong Jiang, Songsong Sun and Xiaolin Gong
Materials 2023, 16(22), 7186; https://doi.org/10.3390/ma16227186 - 16 Nov 2023
Cited by 1 | Viewed by 1150
Abstract
The high-cycle bending fatigue experiment is one of the most important necessary steps in guiding the crankshaft manufacturing process, especially for high-power engines. In this paper, an accelerated method was proposed to shorten the time period of this experiment. First, the loading period [...] Read more.
The high-cycle bending fatigue experiment is one of the most important necessary steps in guiding the crankshaft manufacturing process, especially for high-power engines. In this paper, an accelerated method was proposed to shorten the time period of this experiment. First, the loading period was quickened through the prediction of the residual fatigue life based on the unscented Kalman filtering algorithm approach and the crack growth speed. Then, the accuracy of the predictions was improved obviously based on the modified training section based on the theory of fracture mechanics. Finally, the fatigue limit load analysis result was proposed based on the predicted fatigue life and the modified SAFL (statistical analysis for the fatigue limit) method. The main conclusion proposed from this paper is that compared with the conventional training sections, the modified training sections based on the theory of fracture mechanics can obviously improve the accuracy of the remaining fatigue life prediction results, which makes this approach more suitable for the application. In addition, compared with the system’s inherent natural frequency, the fatigue crack can save the experiment time more effectively and thus is superior to the former factor as the failure criterion parameter. Full article
(This article belongs to the Special Issue Advances in Statistical Analysis of Fatigue Data)
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