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Applied Sciences
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Machine Learning for Fatigue Design
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Machine Learning for Fatigue Design

A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Materials Science and Engineering".

Deadline for manuscript submissions: 30 September 2024 | Viewed by 2266

Special Issue Editors

Dr. Davide S. Paolino

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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. 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. Alberto Ciampaglia

E-Mail Website
Guest Editor Assistant
Department of Mechanical and Aerospace Engineering, Politecnico di Torino, 10129 Torino, Italy
Interests: composite structures; composites; damage assessment; icme; lightweight design; machine learning; mechanistic data science; multiscale; smart materials; structural health monitoring

Special Issue Information

Dear Colleagues,

The fatigue response is governed by concurring phenomena, e.g., applied load, manufacturing defects, material microstructure, component size, residual stresses, that must be accounted for to guarantee a safe design. Statistical and empirical models have been largely preferred to phenomenological approaches over the years, due to the complexity arising from the numerous governing factors and their interactions. The recent advancements in the field of machine learning methods presents an opportunity for innovative data-driven approaches that can predict the fatigue response learning from the experimental observation. The increasing interest of the engineering community to data-driven methods fostered their application to fields, including as structural health monitoring and material modelling, which demonstrate the huge potentiality of data-driven methods to predict complex responses.

This Special Issue aims at collecting the recent advancement in the machine learning application to the fatigue design of structures. Research articles on the application of machine learning methods (e.g., neural networks, gaussian process, SVM) and its integration with phenomenological and empirical knowledge (e.g., physics-informed, theory-guided, physics embedded) to the fatigue design and literature reviews are welcome.

Dr. Davide S. Paolino
Dr. Andrea Tridello
Guest Editors

Dr. Alberto Ciampaglia
Guest Editor Assistant

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. Applied Sciences 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 2400 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
  • machine learning
  • neural networks
  • design
  • structure
  • durability
  • defects
  • crack propagation

Published Papers (2 papers)

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Research

13 pages, 6907 KiB  
Article
A Novel Physics-Guided Neural Network for Predicting Fatigue Life of Materials
by Dexin Yang, Afang Jin and Yun Li
Appl. Sci. 2024, 14(6), 2502; https://doi.org/10.3390/app14062502 - 15 Mar 2024
Viewed by 439
Abstract
A physics-guided neural network (PGNN) is proposed to predict the fatigue life of materials. In order to reduce the complexity of fatigue life prediction and reduce the data required for network training, the PGNN only predicts the fatigue performance parameters under a specific [...] Read more.
A physics-guided neural network (PGNN) is proposed to predict the fatigue life of materials. In order to reduce the complexity of fatigue life prediction and reduce the data required for network training, the PGNN only predicts the fatigue performance parameters under a specific loading environment, and calculates the fatigue life by substituting the load into the fatigue performance parameters. The advantage of this is that the network does not need to evaluate the effect of numerical changes in the load on fatigue life. The load only needs to participate in the error verification, which reduces the dimension of the function that the neural network needs to approximate. The performance of the PGNN is verified using published data. Due to the reduction in the complexity of the problem, the PGNN can use fewer training samples to obtain more accurate fatigue life prediction results and has a certain extrapolation ability for the changes in trained loading environment parameters. The prediction process of the PGNN for fatigue life is not completely a black box, and the prediction results are helpful for scholars to further study the fatigue phenomenon. Full article
(This article belongs to the Special Issue Machine Learning for Fatigue Design)
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21 pages, 4853 KiB  
Article
Assessment of the Critical Defect in Additive Manufacturing Components through Machine Learning Algorithms
by Andrea Tridello, Alberto Ciampaglia, Filippo Berto and Davide Salvatore Paolino
Appl. Sci. 2023, 13(7), 4294; https://doi.org/10.3390/app13074294 - 28 Mar 2023
Cited by 3 | Viewed by 1306
Abstract
The design against fatigue failures of Additively Manufactured (AM) components is a fundamental research topic for industries and universities. The fatigue response of AM parts is driven by manufacturing defects, which contribute to the experimental scatter and are strongly dependent on the process [...] Read more.
The design against fatigue failures of Additively Manufactured (AM) components is a fundamental research topic for industries and universities. The fatigue response of AM parts is driven by manufacturing defects, which contribute to the experimental scatter and are strongly dependent on the process parameters, making the design process rather complex. The most effective design procedure would involve the assessment of the defect population and the defect size distribution directly from the process parameters. However, the number of process parameters is wide and the assessment of a direct relationship between them and the defect population would require an unfeasible number of expensive experimental tests. These multivariate problems can be effectively managed by Machine Learning (ML) algorithms. In this paper, two ML algorithms for assessing the most critical defect in parts produced by means of the Selective Laser Melting (SLM) process are developed. The probability of a defect with a specific size and the location and scale parameters of the statistical distribution of the defect size, assumed to follow a Largest Extreme Value Distribution, are estimated directly from the SLM process parameters. Both approaches have been validated using literature data obtained by testing the AlSi10Mg and the Ti6Al4V alloy, proving their effectiveness and predicting capability. Full article
(This article belongs to the Special Issue Machine Learning for Fatigue Design)
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