Machine tool thermal error is an important reason for poor machining accuracy. Thermal error compensation is a primary technology in accuracy control. To build thermal error model, temperature variables are needed to ...Machine tool thermal error is an important reason for poor machining accuracy. Thermal error compensation is a primary technology in accuracy control. To build thermal error model, temperature variables are needed to be divided into several groups on an appropriate threshold. Currently, group threshold value is mainly determined by researchers experience. Few studies focus on group threshold in temperature variable grouping. Since the threshold is important in error compensation, this paper arms to find out an optimal threshold to realize temperature variable optimization in thermal error modeling. Firstly, correlation coefficient is used to express membership grade of temperature variables, and the theory of fuzzy transitive closure is applied to obtain relational matrix of temperature variables. Concepts as compact degree and separable degree are introduced. Then evaluation model of temperature variable clustering is built. The optimal threshold and the best temperature variable clustering can be obtained by setting the maximum value of evaluation model as the objective. Finally, correlation coefficients between temperature variables and thermal error are calculated in order to find out optimum temperature variables for thermal error modeling. An experiment is conducted on a precise horizontal machining center. In experiment, three displacement sensors are used to measure spindle thermal error and twenty-nine temperature sensors are utilized to detect the machining center temperature. Experimental result shows that the new method of temperature variable optimization on optimal threshold successfully worked out a best threshold value interval and chose seven temperature variables from twenty-nine temperature measuring points. The model residual of z direction is within 3 μm. Obviously, the proposed new variable optimization method has simple computing process and good modeling accuracy, which is quite fit for thermal error compensation.展开更多
It is concluded from the results of testing the frequency characteristics of the sub micron precision machine tool servo control system, that the existence of several oscillating modalities is the main factor that aff...It is concluded from the results of testing the frequency characteristics of the sub micron precision machine tool servo control system, that the existence of several oscillating modalities is the main factor that affects the performance of the control system. To compensate for this effect,several concave filters are utilized in the system to improve the control accuracy. The feasibility of compensating for several oscillating modalities with a single concave filter is also studied. By applying a modified Butterworth concave filter to the practical system, the maximum stable state output error remains under ±10 nm in the closed loop positioning system.展开更多
The dynamics of an ultra‐precision machine tool determines the precision of the machined surface.This study aims to propose an effective method to model and analyze the dynamics of an ultra‐precision fly‐cutting ma...The dynamics of an ultra‐precision machine tool determines the precision of the machined surface.This study aims to propose an effective method to model and analyze the dynamics of an ultra‐precision fly‐cutting machine tool.First,the dynamic model of the machine tool considering the deformations of the cutter head and the lathe head is developed.Then,the mechanical elements are classified into M subsystems and F subsystems according to their properties and connections.The M‐subsystem equations are formulated using the transfer matrix method for multibody systems(MSTMM),and the F‐subsystem equations are analyzed using the finite element method and the Craig-Bampton reduction method.Furthermore,all the subsystems are assembled by combining the restriction equations at connection points among the subsystems to obtain the overall transfer equation of the machine tool system.Finally,the vibration characteristics of the machine tool are evaluated numerically and are validated experimentally.The proposed modeling and analysis method preserves the advantages of the MSTMM,such as high computational efficiency,low computational load,systematic reduction of the overall transfer equation,and generalization of its computational capability to general flexible‐body elements.In addition,this study provides theoretical insights and guidance for the design of ultra‐precision machine tools.展开更多
基金supported by Jiangsu Provincial Prospective Joint Research Foundation for Industry-University-Research of China (Grant No. BY2009102)Henan Provincial Major Scientific and Technological Projects of China (Grant No. 102102210050)
文摘Machine tool thermal error is an important reason for poor machining accuracy. Thermal error compensation is a primary technology in accuracy control. To build thermal error model, temperature variables are needed to be divided into several groups on an appropriate threshold. Currently, group threshold value is mainly determined by researchers experience. Few studies focus on group threshold in temperature variable grouping. Since the threshold is important in error compensation, this paper arms to find out an optimal threshold to realize temperature variable optimization in thermal error modeling. Firstly, correlation coefficient is used to express membership grade of temperature variables, and the theory of fuzzy transitive closure is applied to obtain relational matrix of temperature variables. Concepts as compact degree and separable degree are introduced. Then evaluation model of temperature variable clustering is built. The optimal threshold and the best temperature variable clustering can be obtained by setting the maximum value of evaluation model as the objective. Finally, correlation coefficients between temperature variables and thermal error are calculated in order to find out optimum temperature variables for thermal error modeling. An experiment is conducted on a precise horizontal machining center. In experiment, three displacement sensors are used to measure spindle thermal error and twenty-nine temperature sensors are utilized to detect the machining center temperature. Experimental result shows that the new method of temperature variable optimization on optimal threshold successfully worked out a best threshold value interval and chose seven temperature variables from twenty-nine temperature measuring points. The model residual of z direction is within 3 μm. Obviously, the proposed new variable optimization method has simple computing process and good modeling accuracy, which is quite fit for thermal error compensation.
文摘It is concluded from the results of testing the frequency characteristics of the sub micron precision machine tool servo control system, that the existence of several oscillating modalities is the main factor that affects the performance of the control system. To compensate for this effect,several concave filters are utilized in the system to improve the control accuracy. The feasibility of compensating for several oscillating modalities with a single concave filter is also studied. By applying a modified Butterworth concave filter to the practical system, the maximum stable state output error remains under ±10 nm in the closed loop positioning system.
基金National Natural Science Foundation of China,Grant/Award Number:52105129Science Challenge Project,Grant/Award Number:JZDD2016006–0102Boya Postdoctoral Fellowship of Peking University。
文摘The dynamics of an ultra‐precision machine tool determines the precision of the machined surface.This study aims to propose an effective method to model and analyze the dynamics of an ultra‐precision fly‐cutting machine tool.First,the dynamic model of the machine tool considering the deformations of the cutter head and the lathe head is developed.Then,the mechanical elements are classified into M subsystems and F subsystems according to their properties and connections.The M‐subsystem equations are formulated using the transfer matrix method for multibody systems(MSTMM),and the F‐subsystem equations are analyzed using the finite element method and the Craig-Bampton reduction method.Furthermore,all the subsystems are assembled by combining the restriction equations at connection points among the subsystems to obtain the overall transfer equation of the machine tool system.Finally,the vibration characteristics of the machine tool are evaluated numerically and are validated experimentally.The proposed modeling and analysis method preserves the advantages of the MSTMM,such as high computational efficiency,low computational load,systematic reduction of the overall transfer equation,and generalization of its computational capability to general flexible‐body elements.In addition,this study provides theoretical insights and guidance for the design of ultra‐precision machine tools.