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Zhaoxi Zhao,Yukui Wang,Zhen-long Wang,Jianyong Liu 대한기계학회 2019 JOURNAL OF MECHANICAL SCIENCE AND TECHNOLOGY Vol.33 No.2
Thermal error is a major factor influencing the accuracy of large precision electrical discharge machining (EDM) machine tools, especially when processing continuously for a long time. In this paper, a novel thermal analysis model was set up to identify the static and dynamic thermal behaviour of the large EDM machine tool. The thermal effect of multiple spark discharges is considered. An equivalent heat flux method was proposed to model the intermittent heat flux for the first time. Both the steady and transient analyses were applied to investigate the thermal equilibrium time of critical points. It is found that when the study point is far away from the heat source, the longer thermal equilibrium time is needed. And the thermal equilibrium time of the machine tool was also estimated. Verification experiment has been performed, indicating the simulation accuracy of 87 % on the temperature rise of the electrode. Moreover, on the displacement of the spindle, the simulated result matched with the experimental result in Z direction error of 7 %. Finally, suggestions for reducing the thermal deformation were proposed to further improve the machining accuracy of large EDM machine tools.
Zhaoxi Zhao,Jia Zhang,Yukui Wang,Zhen-long Wang,Jianyong Liu,Chenghao Quan 대한기계학회 2019 JOURNAL OF MECHANICAL SCIENCE AND TECHNOLOGY Vol.33 No.7
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.