The rotation accuracy of the aerostatic spindle can easily be affected by shaft shape errors due to the small gas film clearance. Thus, the main shaft shape errors with the largest scale—that is, the roundness and cylindricity errors—are studied in this paper, and a dynamic mathematical model is established with the consideration of the roundness, cylindricity errors, and spindle speed. In order to construct the shaft model, the discrete coefficient index of the shaft radius based on roundness measurement data are proposed. Then, the simulation calculations are conducted based on the measured cylindricity data and the constructed shaft model. The calculation results are compared with the spindle rotation accuracy measured using the spindle error analyzer. The results show that the shaft with a low discrete coefficient is subjected to less unbalanced force and smaller rotation errors, as obtained by the experiment.

Aimed at improving the low measurement accuracy of the binocular vision sensor along the optical axis in the process of target tracking, we proposed a method for auxiliary correction using a laser-ranging sensor in this paper. In the process of system measurement, limited to the mechanical performance of the two-dimensional turntable, the measurement value of a laser-ranging sensor is lagged. In this paper, the lag information is updated directly to solve the time delay. Moreover, in order to give full play to the advantages of binocular vision sensors and laser-ranging sensors in target tracking, federated filtering is used to improve the information utilization and measurement accuracy and to solve the estimated correlation. The experimental results show that the real-time and measurement accuracy of the laser ranging-assisted binocular visual-tracking system is improved by the direct update algorithm and the federal filtering algorithm. The results of this paper are significant for binocular vision sensors and laser-ranging sensors in engineering applications involving target tracking systems.

The problem of chip breaking and evacuation is the key point of staggered teeth boring and trepanning association (BTA) drilling. The factors that influence chip breaking with staggered teeth BTA deep hole drilling are analyzed by using the chip bending deformation mechanism for chip formation and flow through the rake face and chip breaker. This study investigated the distribution and variation of chip deformation and breaking along drilling conditions, with respect to drilling radius, drilling process parameters, tool wear, and chip breaker geometric parameters. The results show that the tool-chip contact length is about 1.65 times the chip thickness in staggered teeth BTA drilling. The cutting radius of the teeth has a considerable influence on the chip thickness. Compared with the drilling speed, the feed has a greater impact on chip deformation and breaking, and the chip thickness and strain increase with increased feed. Increased drilling depth and tooth wear aggravates the friction state between the chip and the rake face, augments chip thickness and tool-chip contact length, and increases the chip’s strain increment. As the width of chip breaker decreases and the height increases, the chip strain increases and the breaking conditions are improved.

In the modern engineering field, recovering the machined surface topography is important for studying mechanical product function and surface characteristics by using the shape from shading (SFS)-based reconstruction method. However, due to the limitations of many constraints and oversmoothing, the existing SFS-based reconstruction methods are not suitable for machined surface topography. This paper presents a new three-dimensional (3D) reconstruction method of machined surface topography. By combining the basic principle of SFS and the analytic method, the analytic model of a surface gradient is established using the gray gradient as a constraint condition. By efficiently solving the effect of quantization errors and ambiguity of the gray scale on reconstruction accuracy using a wavelet denoising algorithm and image processing technology, the reconstruction algorithm is implemented for machined surface topography. Experimental results on synthetic images and machined surface topography images show that the proposed algorithm can accurately and efficiently recover the 3D shape of machined surface topography.

It is important to characterize surface topography in order to study machined surface characteristics. Due to the features of periodicity and randomness of machined surface topography, the existing topographical parameters may not describe its features accurately. A novel characterization method called the normal declination angle of microfacet-based surface topography is thus proposed for this task. The topography of machined surfaces is measured and the data on the normal declination angle are obtained. Then, surface topography is analyzed via the distribution of the normal declination angle. The lognormal distribution characterization model of machined surface topography is established, and the accuracy of the model is verified by error analysis. The results show that the calculated results of the present characterization model are generally consistent with the distribution of the normal declination angle, where the maximal root mean square errors (RMSE) is 4.5%. Therefore, this study may serve as an effective and novel way to describe the characteristics of the machined surface topography.