TY - JOUR
T1 - A machine learning based approach with an augmented dataset for fatigue life prediction of additively manufactured Ti-6Al-4V samples
AU - Horňas, Jan
AU - Běhal, Jiří
AU - Homola, Petr
AU - Doubrava, Radek
AU - Holzleitner, Martin
AU - Senck, Sascha
N1 - Publisher Copyright:
© 2023
PY - 2023/12/1
Y1 - 2023/12/1
N2 - The article deals with the prediction of fatigue life using a machine learning (ML) approach. The original dataset is based on the parameters of defects obtained by micro-computed tomography (µ-CT) prior to fatigue tests, stress level and the fatigue life of additively manufactured (AM) Ti-6Al-4V samples. As the original dataset is considered too small to train a comprehensive ML model, the study proposed a novel approach for dataset augmentation. Dataset augmentation is done using inverse transform sampling and multivariate radial basis function (RBF) interpolation with various values of the smoothing parameter (λ). Finally, ML model accuracy is improved up to 0.953 of coefficient of determination (R2).
AB - The article deals with the prediction of fatigue life using a machine learning (ML) approach. The original dataset is based on the parameters of defects obtained by micro-computed tomography (µ-CT) prior to fatigue tests, stress level and the fatigue life of additively manufactured (AM) Ti-6Al-4V samples. As the original dataset is considered too small to train a comprehensive ML model, the study proposed a novel approach for dataset augmentation. Dataset augmentation is done using inverse transform sampling and multivariate radial basis function (RBF) interpolation with various values of the smoothing parameter (λ). Finally, ML model accuracy is improved up to 0.953 of coefficient of determination (R2).
KW - Additive manufacturing (AM)
KW - Dataset augmentation
KW - Fatigue life prediction
KW - Machine learning (ML)
KW - Radial basis function (RBF) interpolation
KW - Mikro-Computertomographie
KW - Additive manufacturing
KW - Titanium alloys
KW - Zerstörungsfreie Werkstoffprüfung
KW - Porosität
UR - http://www.scopus.com/inward/record.url?scp=85176265491&partnerID=8YFLogxK
U2 - 10.1016/j.engfracmech.2023.109709
DO - 10.1016/j.engfracmech.2023.109709
M3 - Article
AN - SCOPUS:85176265491
SN - 0013-7944
VL - 293
SP - 1
JO - Engineering Fracture Mechanics
JF - Engineering Fracture Mechanics
M1 - 109709
ER -