TY  - JOUR
T1  - RBFNN Model for Prediction Recognition of Tool Wear in Hard Turing
AU - , Akeel Ali Wannas 
JO  - Journal of Engineering and Applied Sciences
VL  - 3
IS  - 10
SP  - 780
EP  - 785
PY  - 2008
DA  - 2001/08/19
SN  - 1816-949x
DO  - jeasci.2008.780.785
UR  - https://makhillpublications.co/view-article.php?doi=jeasci.2008.780.785
KW  - RBFNN
KW  -wear
KW  -hard turning
KW  -applications
KW  -transmission
KW  -components
AB  - Hard turning technology has been gaining acceptance in many industries throughout the last 2 decades. The trend today is to replace the slow and cost-intensive grinding process with finish hard turning in many industrial applications such as bearings, transmission shafts, axles and engine components, flap gears, landing struts and aerospace engine components. In this study, Radial Basis Function Neural Network (RBFNN) model has been developed for the prediction of the status of the tool wear. Learning data was collected from Experimental setup. The neural network model has 3 input nodes and one output representing process Modeling correlates process state variables to parameters. The process input parameters are Feed rate (F), cutting Speed (S) and Depth of cut (D<SUB>c</SUB>). The process output is state Variable (V<SUB>b</SUB>). Regression analysis between finite element results and values predicted by the neural network model shows the least error.
ER  -