Journal of Engineering and Applied Sciences
ISSN: Online 1818-7803ISSN: Print 1816-949x
Abstract
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 (Dc). The process output is state Variable (Vb). Regression analysis between finite element results and values predicted by the neural network model shows the least error.
How to cite this article:
Akeel Ali Wannas . RBFNN Model for Prediction Recognition of Tool Wear in Hard Turing.
DOI: https://doi.org/10.36478/jeasci.2008.780.785
URL: https://www.makhillpublications.co/view-article/1816-949x/jeasci.2008.780.785