Multivariate interpolation and machine learning models for extreme defects-based fatigue life prediction of Ti6Al4V specimens fabricated by SLM
The result's identifiers
Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F68407700%3A21340%2F25%3A00382216" target="_blank" >RIV/68407700:21340/25:00382216 - isvavai.cz</a>
Result on the web
<a href="https://doi.org/10.1016/j.engfracmech.2024.110756" target="_blank" >https://doi.org/10.1016/j.engfracmech.2024.110756</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.1016/j.engfracmech.2024.110756" target="_blank" >10.1016/j.engfracmech.2024.110756</a>
Alternative languages
Result language
angličtina
Original language name
Multivariate interpolation and machine learning models for extreme defects-based fatigue life prediction of Ti6Al4V specimens fabricated by SLM
Original language description
The paper proposes a novel methodology for extreme defects-based fatigue life prediction of Ti6Al4V specimens fabricated by selective laser melting (SLM) technique. The introduced framework is represented by a traditional ranking method based on the three highest values of maximum stress intensity factor (Kmax) with related defect parameters (size, distance from the free surface, compactness and sphericity), training set augmentation using variational autoencoder (VAE) and optimized data-driven models. The defects were observed using micro-computed tomography (mu -CT) prior to the fatigue tests. As data-driven methods a multivariate interpolation and machine learning (ML) models were employed and tuned using Bayesian optimization algorithm called tree-structured Parzen estimator (TPE). The proposed methodology was validated on the test set and the highest prediction accuracy was achieved by random forest (RF) model with value of coefficient of determination R2test = 0.956. Additionally, the SHapley Additive exPlanations (SHAP) analysis was conducted to gain a deeper insights of applied data-driven models.
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
20501 - Materials engineering
Result continuities
Project
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Continuities
I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace
Others
Publication year
2025
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Data specific for result type
Name of the periodical
Engineering Fracture Mechanics
ISSN
0013-7944
e-ISSN
1873-7315
Volume of the periodical
314
Issue of the periodical within the volume
110756
Country of publishing house
GB - UNITED KINGDOM
Number of pages
26
Pages from-to
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UT code for WoS article
001421357100001
EID of the result in the Scopus database
2-s2.0-85214258699