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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

  • Czech description

Classification

  • Type

    J<sub>imp</sub> - Article in a specialist periodical, which is included in the Web of Science database

  • CEP classification

  • OECD FORD branch

    20304 - Aerospace engineering

Result continuities

  • Project

  • 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

  • UT code for WoS article

    000999617800001

  • EID of the result in the Scopus database

    2-s2.0-85146099044