A Real-Time Neural Network Estimator for Workpiece Thermal Expansion Errors

Abstract

The importance of predicting and reducing thermal expansion errors in workpieces is becoming greater as better precision machining processes are developed. An artificial neural network model to estimate the workpiece thermal expansion errors in real-time during precision machining operations is developed and compared with experimental results. A finite element model of workpiece thermal expansion has been created to predict expansions in a thin cylinder undergoing a turning process. The neural network has been trained using finite element model solutions over a range of conditions to allow for changing machining parameters. To realize “on-line” capability, the measurable values of heat flux into the workpiece, surface heat transfer coefficient, and tool location are used as inputs and the expansion as the output for the neural network. The estimations of the network are compared with experimental results from a turning process on a large diameter aluminum cylinder. There is reasonable agreement between measured and estimated expansions with an average error of 18%. The neural network has not been trained at the cutting conditions used during the experiment. The speed of the neural network estimation is much greater than the solution to the finite element model. The finite element model required over 15 minutes to solve on a Pentium 133Mhz computer. The neural network calculated the expansions easily at 1 Hz during the experiment on the same computer. With real-time estimation using measurable data, compensation can be made in the tool path to correct for these errors. The application of this method to precision machining processes has the capability of greatly reducing the error caused by workpiece thermal expansions.