Improvement of Position Repeatability of a Linear Stage with Yaw Minimization

Recently, due to the miniaturization of electronic products, printed circuit boards (PCBs) have also become smaller. This trend has led to the need for high-precision electrical test equipment to check PCBs for disconnections and short circuits. The purpose of this study is to improve the position repeatability of the platform unit up to ±2.5 μm in linear stage type test equipment. For this purpose, the causes of the position errors of the platform unit are analyzed. The platform unit holding the PCB is driven by a single-axis linear ball screw drive system offset from its geometric center due to design constraints. The yaw rotation of the platform is found to have a dominant effect on position repeatability. To address this problem, adding balancing weights to the platform unit and adjusting the stiffness of the LM Guides are proposed. These methods reduce the yaw rotation by moving the centers of mass and stiffness closer to the linear ball screw actuator. In the verification tests, the position repeatability was decreased to less than ±1.0 μm.

Improvement of Position Repeatability of a Linear Stage with Yaw Minimization

Recently, due to the miniaturization of electronic products, printed circuit boards (PCBs) have also become smaller. This trend has led to the need for high-precision electrical test equipment to check PCBs for disconnections and short circuits. The purpose of this study is to improve the position repeatability of the platform unit up to ±2.5 μm in a linear stage type test equipment. For this purpose, the causes of position errors of the platform unit are analyzed. The platform unit holding the PCB is driven by a single-axis linear ball screw drive system offset from its geometric center due to design constraints. The yaw rotation of the platform is found to have a dominant effect on position repeatability. To address this problem, the methods of adding balancing weights to the platform unit and adjusting the stiffness of LM Guides are proposed. This reduces the yaw rotation by moving the centers of mass and stiffness closer to the linear ball screw actuator. In the verification tests, the position repeatability was decreased to less than ±1.0 μm.

Structure optimization of a pipetting device to improve the insertion effect of tips

In order to solve the problems of the failure of disposable tip insertion which happens in the pipetting process of most multi-station and high-throughput pipetting devices, this paper proposes a high-rigidity screw-type pipette shaft–disposable tip assembly mechanism with excellent auto-centering effects based on the principle of the ball screw drive. The stiffness model of the new pipetting device is established, and its stiffness and axial deformation are analyzed. This new mechanism was introduced to a multi-station and high-throughput pipetting workstation, and the process of pipetting disposable tips is simulated by ANSYS software. The analytical results show that the stiffness value of the new pipetting device is approximately 90 N/µm, and the amount of deformation of the z-axis manipulator is reduced by about 60 % compared to the original pipetting device. Finally, physical verification of the prototype was carried out in the work. The test results show that the new pipetting workstation can increase the tightening rate of the tips by approximately 12 % after optimization when 96 tips are inserted in a single press. In addition, the pass rate of the tightness test of the optimized pipetting workstation has increased by approximately 20 %.

The influence of ball screw drive positioning errors on the accuracy of part manufacturing

The article considers the components of the total error of mechanical processing that occur when positioning the working units of the machine: ball screw drive elements. The reasons for the loss of positioning accuracy of the machine drives are described. The accuracy of the positioning of the machine spindle in determining the axes of the holes to be processed is analyzed. The numerical estimation of the values of the errors of the temperature deformations of the lead screw is carried out on the example of drilling holes in the workpiece. The causes of heating of the ball screw drive of the machine are identified. The dependence of the unit heating on the speed of movement of the operating elements of the machine is described. The optimal trajectory of the tool movement when processing holes in the workpiece is presented. The criterion of optimality of this trajectory is described. The values of the deviations of each hole in the workpiece from the specified accuracy of their location are obtained. The scheme of accumulation of errors of linear displacements resulting from the temperature deformation of the lead screw of the CNC machine drive is presented. The value of the accumulated total error of the temperature deformations of the ball screw pair is obtained. The error associated with the movement of the machine drive carriage is considered. The geometric characteristics of the carriage orientation are given. The schemes of occurrence of the error caused by the change of the roll angle and the carriage tilt angle are presented. The maximum axial load of the lead screw at translational acceleration is calculated. The scheme of possible carriage deflection under the action of the maximum translational force of a ball screw pair is presented. The numerical estimation of the maximum possible roll angle of the carriage, as well as the maximum deviation from the specified accuracy of the carriage, at the maximum load on the lead screw, is carried out. As a result, it is concluded that the total error of the machine drives positioning can go beyond the tolerances of the linear dimensions of the processed holes, which significantly affects the accuracy of the part manufacturing.

Dynamic Modeling of a Ball-Screw Drive and Identification of Its Installation Parameters

Parametric expressions of equivalent stiffnesses of a ball-screw shaft are obtained by derivation of its geometric parameters, the finite element method (FEM), and data fitting based on a modified probability density function of log-normal distribution. A dynamic model of a ball-screw drive that considers effects of bearing stiffnesses, the mass of the nut, and the axial pretension is established based on equivalent stiffnesses of its shaft. With the dynamic model and modal experimental results obtained by Bayesian operational modal analysis (BOMA), installation parameters of the ball-screw drive are identified by a genetic algorithm (GA) with a new comprehensive objective function that considers natural frequencies, mode shapes, and flexibility of the ball-screw drive. The effectiveness of the methodology is experimentally validated.

Online Chatter Detection for Milling Operations Using LSTM Neural Networks Assisted by Motor Current Signals of Ball Screw Drives

For certain combinations of cutter spinning speeds and cutting depths in milling operations, self-excited vibrations or chatter of the milling tool are generated. The chatter deteriorates the surface finish of the workpiece and reduces the useful working life of the tool. In the past, extensive work has been reported on chatter detections based on the tool deflection and sound generated during the milling process, which is costly due to the additional sensor and circuitry required. On the other hand, the manual intervention is necessary to interpret the result. In the present research, online chatter detection based on the current signal applied to the ball screw drive (of the CNC machine) has been proposed and evaluated. There is no additional sensor required. Dynamic equations of the process are improved to simulate vibration behaviors of the milling tool during chatter conditions. The sequence of applied control signals for a particular feed rate is decided based on known physical and control parameters of the ball screw drive. The sequence of the applied control signal to the ball screw drive for a particular feed rate can be easily calculated. Hence, costly experimental data are eliminated. Long short-term memory neural networks are trained to detect the chatter based on the simulated sequence of control currents. The trained networks are then used to detect chatter, which shows 98% of accuracy in experiments.