Friction energy consumption is the primary cause of energy loss in rolling bearings, and friction characteristics are critical indicators of rolling bearing quality. To comprehensively understand the friction characteristics of ball bearings, the equivalent friction coefficient was proposed, and a reliable measurement method was studied. This new solution addressed the difficulty of measuring the friction characteristics of ball bearings highlighted by friction torque. The angular speeds of various components in the rolling bearings were derived using a quasistatic approach. The angular speed relationships among various components of the rolling bearings were subsequently analysed. A kinetic energy dissipation model for the measuring system was ultimately obtained. A novel apparatus for measuring the rolling bearing equivalent friction coefficient was established. The spindle only underwent angular speed attenuation due to friction of the tested bearing without the use of power, and the variation in kinetic energy was monitored in real time with a high-precision speed sensor. After that, the equivalent friction coefficients of the measured bearings were examined. The effects of speed, load, and lubrication on the equivalent friction coefficient of the tested bearing were studied. The findings demonstrated that, to some extent, the equivalent friction coefficient increased with an increase in spindle speed and decreased with increasing load. The equivalent friction coefficient also increased with increasing kinematic viscosity of the lubrication oil, and the friction coefficient for dry friction was greater than that with 50 oil but slightly lower than that with 70 oil. With this method, an accurate and comprehensive understanding of the friction characteristics of ball bearings is achieved.

Polycrystalline materials are extensively employed in industry. Its surface roughness significantly affects the working performance. Material defects, particularly grain boundaries, have a great impact on the achieved surface roughness of polycrystalline materials. However, it is difficult to establish a purely theoretical model for surface roughness with consideration of the grain boundary effect using conventional analytical methods. In this work, a theoretical and deep learning hybrid model for predicting the surface roughness of diamond-turned polycrystalline materials is proposed. The kinematic–dynamic roughness component in relation to the tool profile duplication effect, work material plastic side flow, relative vibration between the diamond tool and workpiece, etc, is theoretically calculated. The material-defect roughness component is modeled with a cascade forward neural network. In the neural network, the ratio of maximum undeformed chip thickness to cutting edge radius R_TS, work material properties (misorientation angle θ_g and grain size d_g), and spindle rotation speed n_s are configured as input variables. The material-defect roughness component is set as the output variable. To validate the developed model, polycrystalline copper with a gradient distribution of grains prepared by friction stir processing is machined with various processing parameters and different diamond tools. Compared with the previously developed model, obvious improvement in the prediction accuracy is observed with this hybrid prediction model. Based on this model, the influences of different factors on the surface roughness of polycrystalline materials are discussed. The influencing mechanism of the misorientation angle and grain size is quantitatively analyzed. Two fracture modes, including transcrystalline and intercrystalline fractures at different R_TS values, are observed. Meanwhile, optimal processing parameters are obtained with a simulated annealing algorithm. Cutting experiments are performed with the optimal parameters, and a flat surface finish with Sa 1.314 nm is finally achieved. The developed model and corresponding new findings in this work are beneficial for accurately predicting the surface roughness of polycrystalline materials and understanding the impacting mechanism of material defects in diamond turning.