Atıf İçin Kopyala
Yüce Z., Yayla P., Taşkın A.
DEFENCE SCIENCE JOURNAL, cilt.74, sa.1, ss.119-126, 2024 (SCI-Expanded)
-
Yayın Türü:
Makale / Tam Makale
-
Cilt numarası:
74
Sayı:
1
-
Basım Tarihi:
2024
-
Doi Numarası:
10.14429/dsj.74.18954
-
Dergi Adı:
DEFENCE SCIENCE JOURNAL
-
Derginin Tarandığı İndeksler:
Science Citation Index Expanded (SCI-EXPANDED), Scopus, Aerospace Database, Applied Science & Technology Source, Aquatic Science & Fisheries Abstracts (ASFA), Communication Abstracts, Compendex, Computer & Applied Sciences, Metadex, zbMATH, Civil Engineering Abstracts
-
Sayfa Sayıları:
ss.119-126
-
Yıldız Teknik Üniversitesi Adresli:
Evet
Özet
using the Fighter Aircraft Loading Standard for Fatigue (FALSTAFF) to calculate crack growth life. Considering
particular service load conditions, ninety different spectra are developed, and the crack growth life of the joint is
calculated based on linear elastic fracture mechanics correspondingly. Also, to simulate the worst-case scenario,
friction between members and the retardation effect of load spectra are not considered when calculating crack growth
life. Python’s Tensor Flow and Scikit-learn libraries are utilised to build machine learning models. Then, ninety
different load spectra are input for the thru-crack configuration to predict the crack propagation life. Eventually, the
crack propagation life predictions of random forest regression and artificial neural network models are compared.
The findings indicate that permutation feature importance and hyperparameter-optimisation significantly affect the
model’s accuracy and processing time performance