Modeling of cutting forces as function of cutting parameters for face milling of satellite 6 using an artificial neural network
✍ Scribed by Ş. Aykut; M. Gölcü; S. Semiz; H.S. Ergür
- Publisher
- Elsevier Science
- Year
- 2007
- Tongue
- English
- Weight
- 400 KB
- Volume
- 190
- Category
- Article
- ISSN
- 0924-0136
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✦ Synopsis
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 , m/min), feed rate (f, mm/min), depth of cut (a p , 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.