Estimation of surface roughness and cutting speed in CNC WEDM by artificial neural network that employed trainable activation function
Küçük Resim Yok
Tarih
2021-02-01
Yazarlar
Dergi Başlığı
Dergi ISSN
Cilt Başlığı
Yayıncı
SAGE Journals
Erişim Hakkı
info:eu-repo/semantics/openAccess
Attribution-NonCommercial-ShareAlike 3.0 United States
Attribution-NonCommercial-ShareAlike 3.0 United States
Özet
Activation functions are the most significant properties of artificial neural networks (ANN) because these functions are directly related with the ability of ANN in learning or modelling a system or a function. Furthermore, another reason for the significance of the fact that determination of optimal activation function in ANN is its relationship with success level. In this experimental study, the effects of different types of wire electrodes, cooling techniques and workpiece materials on surface roughness (Ra) and cutting speed (Vc) in wire electrical discharge machining (WEDM) were investigated by using trainable activation functions (AFt) and modelling them in ANNs. So far, a number of methods have been performed according to the data set in order to optimally predict Ra and Vc results. Among these methods, randomized ANN with AFt was found to be the best one for robust prediction according to RMSE values. While the value was 0.280 for Vc, it was 0.2104 for Ra. Optimum activation functions in Ra and Vc were found at first and third degree trainable functions, respectively.
Açıklama
Anahtar Kelimeler
Random Weight Artificial Neural Network, Trainable Activation Function, Artificial Neural Network, WEDM, Surface Roughness, Cutting Speed
Kaynak
WoS Q Değeri
Q3
Scopus Q Değeri
Q2
Cilt
235
Sayı
15
Künye
Gürbüz, H. (2021). Estimation of surface roughness and cutting speed in CNC WEDM by artificial neural network that employed trainable activation function. Proceedings of the Institution of Mechanical Engineers, Part C: Journal of Mechanical Engineering Science, 235 (15). pp.2737-2735. DOI:https://dx.doi.org/10.1177/0954406221990057
