Dynamic thermal behavior and thermal error prediction of spindle due to periodic jump motions in a large precision die-sinking EDM machine

Abstract

Thermal error which has been widely studied in cutting machine tools, was ignored in the EDM machines in most cases, since there is usually no high-speed rotation for spindles. However, for large die-sinking EDM machines, due to heavy load of drive system and long processing cycle of large aeronautical parts, thermal error induced by jump motion has seriously impaired the machining accuracy and gradually been recognized. In this paper, the dynamic thermal behavior of spindle induced by periodic jump motions in large precision die-sinking EDM machine was studied for the first time. Noted that the Z-axis base and column show obvious temperature rise and the thermal error in Y direction is the largest, which is about 6.5 and 5 times compared with that in X and Z directions. Based on this, an efficient thermal error prediction model was presented. Thermal sensitive points were picked out through fuzzy clustering and correlation theory, taken as inputs of radial basis function (RBF) neural network to guarantee the accuracy. As a result, the prediction accuracy in X, Y and Z directions are 95.2 %, 92.5 % and 94.4 %, respectively. Finally, the effect of jump period on spindle thermal behavior was investigated, and suggestions for optimizing jump motion parameters were proposed to further improve the machining accuracy of large EDM machines.