RBF neural network for thrust and torque predictions in drilling operations

Abstract

In recent years, radial basis function (RBF) neural networks have been shown to be versatile for performance prediction involving nonlinear processes. Machining performance prediction involving various process variables is a nonlinear problem. The conventional mechanics of the cutting approach for predicting thrust and torque in drilling makes use of the oblique cutting theory and an orthogonal cutting databank. The quantitative reliability, in these models, depends on the 'input parameters' along with the 'edge force' components from the orthogonal cutting databank for that given work material. By contrast, neural networks for drilling performance prediction have been shown to be successful for quantitative predictions with minimum number of inputs. In this paper, an RBF neural network architecture is proposed which uses process variables such as tool geometry and operating conditions to estimate thrust and torque in drilling. Extensive drilling tests are carried out to train the RBF network. The developed network is tested over a range of process variables to estimate thrust and torque. It is shown that, using the neural network architecture, the drilling forces are 'simultaneously' predicted to within 5% of the experimental values.