RBF Neural Network for Thrust and Torque Predictions in Drilling Operations

• Venue: ICCIMA '99 Proceedings of the 3rd International Conference on Computational Intelligence and Multimedia Applications
• Year: 1999

In recent years, RBF neural networks have been shown to be versatile for performance prediction involving non-linear processes. Machining performance prediction involving various process variables is a non-linear problem. Conventional mechanics of cutting approach for prediction of thrust and torque in drilling makes use of the oblique cutting theory and orthogonal cutting data bank. The quantitative reliability, in these models, depends on the 'input parameters' along with the 'edge force' components from the orthogonal cutting data bank 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 radial basis function (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 in this work that using the neural network architecture the drilling forces are 'simultaneously' predicted within 5% of the experimental values.