Modelling fatigue life prediction of additively manufactured Ti-6Al-4V samples using machine learning approach
The result's identifiers
Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00010669%3A_____%2F23%3AN0000005" target="_blank" >RIV/00010669:_____/23:N0000005 - isvavai.cz</a>
Result on the web
<a href="https://www.sciencedirect.com/science/article/pii/S0142112322007332" target="_blank" >https://www.sciencedirect.com/science/article/pii/S0142112322007332</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.1016/j.ijfatigue.2022.107483" target="_blank" >10.1016/j.ijfatigue.2022.107483</a>
Alternative languages
Result language
angličtina
Original language name
Modelling fatigue life prediction of additively manufactured Ti-6Al-4V samples using machine learning approach
Original language description
In this work, a framework based on the machine learning (ML) approach and Spearman’s rank correlation analysis is introduced as an effective instrument to solve the influence of defects detected by micro-computed tomography (μCT) method, and stress amplitude on the fatigue life performance of AM Ti-6Al-4V. Artificial neural network (ANN), random forest regressor (RFR) and support vector regressor (SVR) models are implemented and optimized. The optimization is performed on training set by tuning the hyperparameters and parameters using the leave-one-out cross validation (LOOCV) technique. The results present comparison between predicted and experimental results and validate the proposed framework.
Czech name
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Czech description
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Classification
Type
J<sub>imp</sub> - Article in a specialist periodical, which is included in the Web of Science database
CEP classification
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OECD FORD branch
20304 - Aerospace engineering
Result continuities
Project
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Continuities
R - Projekt Ramcoveho programu EK
Others
Publication year
2023
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Data specific for result type
Name of the periodical
International Journal of Fatigue
ISSN
0142-1123
e-ISSN
0142-1123
Volume of the periodical
169
Issue of the periodical within the volume
4/2023
Country of publishing house
GB - UNITED KINGDOM
Number of pages
12
Pages from-to
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UT code for WoS article
000999617800001
EID of the result in the Scopus database
2-s2.0-85146099044