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

Dergi Başlığı

Dergi ISSN

Cilt Başlığı

Yayıncı

SAGE Journals

Erişim Hakkı

info:eu-repo/semantics/openAccess
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