Prediction of the optimal FSW process parameters for joints using machine learning techniques
Küçük Resim Yok
Tarih
2021
Yazarlar
Dergi Başlığı
Dergi ISSN
Cilt Başlığı
Yayıncı
Walter de Gruyter GmbH
Erişim Hakkı
info:eu-repo/semantics/closedAccess
Özet
In this work, a couple of dissimilar AA2024/AA7075 plates were experimentally welded for the purpose of considering the effect of friction-stir welding (FSW) parameters on mechanical properties. First, the main mechanical properties such as ultimate tensile strength (UTS) and hardness of welded joints were determined experimentally. Secondly, these data were evaluated through modeling and the optimization of the FSW process as well as an optimal parametric combination to affirm tensile strength and hardness using a support vector machine (SVM) and an artificial neural network (ANN). In this study, a new ANN model, including the Nelder-Mead algorithm, was first used and compared with the SVM model in the FSW process. It was concluded that the ANN approach works better than SVM techniques. The validity and accuracy of the proposed method were proved by simulation studies.
Açıklama
Anahtar Kelimeler
Friction-stir welding, ANN, SVM, aluminum alloys, nelder-mead nonlinear optimization algorithm
Kaynak
Materials Testing
WoS Q Değeri
Q2
Scopus Q Değeri
Cilt
Sayı
Künye
Sarsilmaz, F., & Kavuran, G. (2021). Prediction of the optimal FSW process parameters for joints using machine learning techniques. Materials Testing, 63(12), 1104-1111.
