Dongya Zhang

Tribological Properties of Self-Lubricating Polymer–Steel Laminated Composites

Self-lubricating polymer–steel laminated composites (SLC) consisting of matrix zones and filled zones were fabricated by a laminating–bonding process. The matrix zones were silicon steel sheets and the filled zones were polymer matrix filled with MoS2 and graphite, respectively. The control specimen was prepared by spraying a polymer composite coating on a GCr15 disc. The tribological properties of SLC were investigated using a ball-on-disc tribometer under different loads and frequencies. Compared to the control specimen, the friction coefficient and wear rate of SLC was reduced by 57% and threefold at 4 N and 6 Hz, respectively. In addition, the friction coefficient of SLC was low and stable under low reciprocating frequency, and it was high and fluctuating under high reciprocating frequency. In addition, the wear rate increased with increasing applied load and reciprocating frequency. Scanning electron microscopy (SEM) images show that the lubricating mechanism of SLC was that solid lubricants embedded in filled zones expanded and smeared a layer of transfer film on the sliding path to lubricate the surface. The thermal expansion of solid lubricants was simulated using ANSYS software with thermal-stress coupling. The simulation results showed the maximum temperature of the filled zones was 130°C, and the maximum normal displacement of solid lubricants was approximately 10 μm. This confirmed that the solid lubricants expanded effectively by the aid of frictional heat.

Thermal Errors Classification Compensation without Sensor for CNC Machine Tools

Both multicollinearity and utilization deficiency of temperature sensors affect the robustness and the prediction precision of traditional thermal error prediction models. To address the problem, a thermal error prediction model without temperature sensors is proposed. Firstly, the paper analyzes the temperature field and thermal deformation mechanisms of the spindle of a CNC gear grinding machine in accordance with the parameters, efficiencies, and structures of the machine spindle and bearing. A preliminary theoretical model is established on the basis of the mechanism analysis. Secondly, the theoretical model is corrected according to the actual operation parameters of the machine. Thirdly, verification experiments are carried out on machine tools of the same type. It is found that the corrected model has higher precision in predicting thermal errors at the same rotational velocity. The standard deviation and the maximum residual error are reduced by at least 39% and 48% separately. The prediction precision decreases with the increase in prediction range when predicting thermal errors at different rotational velocities. The model has high prediction precision and strong robustness in the case of reasonable prediction range and classified prediction. In a word, prediction precision and robustness of the model without temperature sensors can be effectively ensured by reasonably determining the prediction range and practicing classified prediction and compensation for thermal errors at different rotational velocities. The model established can be applied to machine tools that have difficulties in arranging sensors or those whose sensors are significantly disturbed.