Modeling and prediction of surface roughness in turning operations using artificial neural network and multiple regression method
Küçük Resim Yok
Tarih
2011
Yazarlar
Dergi Başlığı
Dergi ISSN
Cilt Başlığı
Yayıncı
PERGAMON-ELSEVIER SCIENCE LTD
Erişim Hakkı
info:eu-repo/semantics/closedAccess
Özet
Machine parts during their useful life are significantly influenced by surface roughness quality. The machining process is more complex, and therefore, it is very hard to develop a comprehensive model involving all cutting parameters. In this study, the surface roughness is measured during turning at different cutting parameters such as speed, feed, and depth of cut. Full factorial experimental design is implemented to increase the confidence limit and reliability of the experimental data. Artificial neural networks (ANN) and multiple regression approaches are used to model the surface roughness of AISI 1040 steel. Multiple regression and neural network-based models are compared using statistical methods. It is clearly seen that the proposed models are capable of prediction of the surface roughness. The ANN model estimates the surface roughness with high accuracy compared to the multiple regression model. (C) 2010 Elsevier Ltd. All rights reserved.
Açıklama
Anahtar Kelimeler
Predictive modeling, Turning operations, Artificial neural networks, Surface roughness
Kaynak
EXPERT SYSTEMS WITH APPLICATIONS
WoS Q Değeri
Q1
Scopus Q Değeri
Q1
Cilt
38
Sayı
5
