Modeling of cutting forces as function of cutting parameters for face milling of satellite 6 using an artificial neural network


Aykut S., Golcu M., Semiz S., Ergur H. S.

JOURNAL OF MATERIALS PROCESSING TECHNOLOGY, vol.190, pp.199-203, 2007 (SCI-Expanded) identifier identifier

  • Publication Type: Article / Article
  • Volume: 190
  • Publication Date: 2007
  • Doi Number: 10.1016/j.jmatprotec.2007.02.045
  • Journal Name: JOURNAL OF MATERIALS PROCESSING TECHNOLOGY
  • Journal Indexes: Science Citation Index Expanded (SCI-EXPANDED), Scopus
  • Page Numbers: pp.199-203
  • Keywords: stellite 6, face milling, cutting forces, artificial neural networks, TOOL LIFE, OPTIMIZATION, MICROSTRUCTURE, WEAR
  • Eskisehir Osmangazi University Affiliated: Yes

Abstract

In this study, artificial neural networks (ANNs) was used for modeling the effects of machinability on chip removal cutting parameters for face milling of stellite 6 in asymmetric milling processes. Cutting forces with three axes (F-x, F-y and F-z) were predicted by changing cutting speed (V-c), feed rate (f) and depth of cut (a(p)) under dry conditions. Experimental studies were carried out to obtain training and test data and scaled conjugate gradient (SCG) feed-forward back-propagation algorithm was used in the networks. Main parameters for the experiments are the cutting speed (V-c n/min), feed rate (f, mm/min), depth of cut (ap, mm) and cutting forces (F-x, F-y and F-z, N). V-c, f and a(p) were used as the input dataset while F-x, F-y and F-z were used as the output dataset. Average percentage error (APEs) values for F-x, F-y and F-z using the proposed model were obtained around 2 and 10% for training and testing, respectively. These results show that the ANNs can be used for predicting the effects of machinability on chip removal cutting parameters for face milling of stellite 6 in asymmetric milling processes. (c) 2007 Published by Elsevier B.V.