Beijing Machine Tool Research Institute

In feed drive mechanisms, thermally-induced deformation in the screw-nut pair affects positioning accuracy. Traditional circulating cooling systems regulate screw shaft temperature but fail to address the uneven axial temperature gradient caused by the nut's reciprocating motion, leading to significant positioning errors. To tackle these issues, a novel thermal error control strategy is devised, utilizing a sintered core heat pipe (SCHP) for cooling the moving nut. Specifically, a gas-liquid phase transition model is formulated for the proposed sintered core heat pipe. Furthermore, the heat transfer performance of the sintered core heat pipe is experimentally validated, and a response surface model is constructed to determine its convective heat transfer coefficient. Following this, by integrating into sintered core heat pipe into feed drive mechanisms, the peak temperature of moving nut, thermal equilibrium time, and screw shaft's elongation were measured to be 26.8 °C, 148 min, and 55.1 μm, respectively. The moving nut's peak temperature, thermal equilibrium time, and screw shaft thermal elongation are significantly reduced by 20 %, 28 %, and 23.6 %, respectively. Positioning error reduction in feed drive mechanisms ranges from 74.8 % to 88.7 %. More importantly, the proposed SCHP-cooled strategy is much more effective than traditional circulating cooling system. • Thermal error control strategy is proposed, utilizing sintered core heat pipe. • Sintered core heat pipe is designed towards thermal error control. • Sintered core heat pipe is embedded into cooling jacket. • Fluid-thermal-solid interaction characteristic models are established. • Effect of heat pipe on thermal behavior of feed drive mechanism is studied.

The profile accuracy of arc grinding wheels directly affects the precision of complex optics during grinding. However, existing studies have yet to systematically elucidate the distinct effects of wheel profile errors across different frequency bands on workpiece form errors. In this study, the generation mechanism of workpiece form error during the arc-envelope grinding process is revealed, considering the influence of wheel profile error across different frequency bands through kinematic simulation. The mapping relationship between profile errors and form errors is further analyzed, showing that the mapping coefficient is governed by both amplitude and frequency. The effects of neglecting wheel profile errors on the compensation effectiveness are analyzed for the conventional form error reverse compensation methods, revealing that conventional approaches cannot achieve complete convergence of error profiles and magnitudes. To address this issue, what we believe to be a novel compensation method considering profile errors of arc grinding wheels is proposed. Experimental results demonstrate that the cylindrical workpiece form error was significantly reduced from 8.8 µm PV to 2.1 µm PV after only a single compensation iteration, achieving a 38.2% improvement in compensation accuracy compared to the conventional shape-correction compensation method after two iterations. This method effectively enhances the grinding precision of complex optics while reducing iteration cycles of grinding-measurement-compensation processes.

Abstract This study aims to investigate the effect of porous material properties on the tilt bearings characteristics. The tilt state of the bearing has a significant effect on the pressure distribution in the air film domain. The stiffness reaches its maximum value of 483 Nμm and the rotational stiffness is 9.02×105 Nm rad when the gas film thickness is 6 μm, the porous material thickness is 8 mm, the circle width is 25 mm and the permeability is 2×10—15 m2. Changes in the permeability of the porous material have a greater effect on the bearing characteristics than changes in other bearing parameters, with the increased rate of the gas mass flow reaching 146%. For the characteristics of tilt porous mass aerostatic bearings, the porous permeability and the thickness are strong sensitivity parameters, and the circle width is the weak sensitivity parameter.

In fast memory response training, predicting behavioral performance through EEG data can provide a technical basis for people selection and decision-making assistance. Therefore, this article models the relationship between EEG data and behavioral performance, and constructs a correlation model between EEG and behavior. The research results indicate that the human-machine operation performance prediction model based on the mental fatigue index and EEG characteristics has a confidence level of over 80%. Using a logistic regression model, the probability of predicting the correctness of manipulation based on the coupling of mental fatigue index and EEG fatigue evaluation index system can reach 89.85%; Using a transfer learning neural network model based on multi head attention mechanism, the probability of predicting perceptual response time based on the coupling of mental fatigue index and EEG fatigue evaluation index system can reach 83.3%.