open-access-imgOpen AccessUsing Intelligent Technique Rbfnn for Prediction Recognition of Tool Wear in Hard Turning
Akeel Ali Wannas
Publication year2013
Publication title
diyala journal of engineering sciences
Resource typeJournals
Hard turning technology has been gaining acceptance in many industries throughout the last 2decades. 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.
Subject(s)aerospace , aerospace engineering , artificial intelligence , artificial neural network , automotive engineering , computer science , engineering , operating system , process (computing) , radial basis function

Seeing content that should not be on Zendy? Contact us.

The content you want is available to Zendy users.

Already have an account? Click here to sign in.
Having issues? You can contact us here