Modelling of Thrust Forces in Drilling of AISI 316 Stainless Steel Using Artificial Neural Network and Multiple Regression Analysis
Yükleniyor...
Dosyalar
Tarih
2012
Yazarlar
Dergi Başlığı
Dergi ISSN
Cilt Başlığı
Yayıncı
Assoc Mechanical Engineers Technicians Slovenia
Erişim Hakkı
info:eu-repo/semantics/openAccess
Özet
In this study, the effects of cutting parameters (i.e., cutting speed, feed rate) and deep cryogenic treatment on thrust force (Ff) have been investigated in the drilling of AISI 316 stainless steel. To observe the effects of deep cryogenic treatment on thrust forces, M35 HSS twist drills were cryogenically treated at -196 degrees C for 24 h and tempered at 200 degrees C for 2 h after conventional heat treatment. The experimental results showed that the lowest thrust forces were measured with the cryogenically treated and tempered drills. In addition, artificial neural networks (ANNs) and multiple regression analysis were used to model the thrust force. The scaled conjugate gradient (SCG) learning algorithm with the logistic sigmoid transfer function was used to train and test the ANNs. The ANN results showed that the SCG learning algorithm with five neurons in the hidden layer produced the coefficient of determinations (R-2) of 0.999907 and 0.999871 for the training and testing data, respectively. In addition, the root mean square error (RMSE) was 0.00769 and 0.009066, and the mean error percentage (MEP) was 0.725947 and 0.930127 for the training and testing data, respectively.
Açıklama
WOS: 000306718800007
Anahtar Kelimeler
artificial neural networks, regression analysis, cryogenic treatment, machining, thrust force, predictive modelling
Kaynak
Strojniski Vestnik-Journal Of Mechanical Engineering
WoS Q Değeri
Q2
Scopus Q Değeri
Q3
Cilt
58
Sayı
07.Aug
