Renke Kang

To address the issues of limited field of view, complex surface reflection and easy accumulation of multi-view stitching errors in the manufacturing and inspection of large-scale components, a high-accuracy multi-view 3D shape measurement method for large-scale components is proposed is proposed in this paper. A positive-negative complementary Gray code phase-shifting structured light coding method is developed in the single-view 3D reconstruction step of the proposed method. Combined with the robust pixel classification and the adaptive Gaussian filtering strategy, it effectively enhances the robustness of absolute phase unwrapping under complex reflection conditions and reduces the single-view geometric reconstruction error. An Levenberg-Marquardt-optimized iterative closest point (LM-ICP) point cloud fine registration algorithm integrating common region constraint and normal vector consistency constraint is constructed, which significantly suppresses the accumulation of registration errors caused by mismatches in multiple stitching processes, in the multi-view point cloud stitching step. Meanwhile, the influences of the coding mode, the number of multi-views, the camera resolution and the exposure time on the measurement accuracy of the proposed method are analyzed. Measurement experiments on dual-ball-bar standard artifacts and typical industrial large-scale components verify that the proposed coding method improves the single-view reconstruction accuracy by about 24.5%, and the LM-ICP point cloud fine registration algorithm increases the overall scale measurement accuracy by about 29.6%. The system achieves stable sub-millimeter measurement accuracy under multi-view conditions.

Chemical mechanical polishing (CMP) is a critical technique for fabricating ultra-smooth and high-quality surfaces of single crystal diamond (SCD), where processing parameters profoundly influence polishing performance. To achieve superior diamond surface finishes, this study first investigates the effects of key process parameters, including oxidant concentration, catalyst type, and abrasive particle size, on surface quality through single-factor experiments. Subsequently, an Archimedes optimization algorithm (AOA)-based prediction model for diamond CMP surface roughness (Sa) is developed and validated experimentally. Results reveal that high-concentration oxidants, fine-particle abrasives, and dual-catalyst polishing systems synergistically enhance surface quality. The AOA-based prediction model demonstrates a root-mean-square error (RMSE) of 0.006 and a correlation coefficient (R) of 0.98 between the predicted and experimental Sa values. Under the conditions of a dual-catalyst type, 35% oxidant concentration, and 500 nm abrasive particle size, the model predicts a surface roughness of 0.128 nm, with an experimental value of 0.125 nm and a relative error of less than 3%. These findings highlight the capability of the model to accurately forecast surface roughness across diverse process parameters, offering a novel predictive framework for precision CMP of SCD.

The influence of axial ultrasonic vibration (AUV) on the surface and subsurface damage characteristics of reaction-bonded silicon carbide (RB-SiC) was analysed through the comparative experiments of Axial ultrasonic assisted end grinding (AUAEG) and conventional grinding (CG) under different grinding parameters, the results show that the surface of RB-SiC after AUAEG has brittle and plastic mixed characteristics simultaneously. AUV can improve the surface quality of the mixed phase but also provides more energy for crack nucleation and propagation, causing more material to fall out and increasing the depth of damage. Under the same conditions, the surface roughness of AUAEG is 10% to 35% greater than that of CG.

A femtosecond laser method for high-quality and high-efficiency micro-hole fabrication of SiC_f/SiC composites is proposed for this difficult-to-machine material. The influence of optical wedge parameters in the femtosecond laser rotary optical system on the size of the generated annular beam spot and the beam deflection angle was investigated. By implementing two-step rotary drilling, micro-holes with an entrance diameter of 501.4 μm, an exit diameter of 499.6 μm, a taper of 0.02°, and good quality at both ends were achieved. The evolution process and mechanism of hole formation in two-step rotary drilling has been demonstrated. Material removal and the phase changes of elements were studied, and no significant processing damage was observed in the cross-sections of SiC fibers and SiC matrix. By employing the two-step rotary drilling method, micro-holes with an aspect ratio of 6 were fabricated within a processing time of 145 s. This work provides technical support for high-quality and high-efficiency processing of SiC_f/SiC composites using femtosecond laser technology.

With the growing demand for industrial robots in the aerospace manufacturing process, the lack of positioning accuracy has become a critical factor limiting their broad application in precision manufacturing. To enhance robot positioning accuracy, one crucial approach is to analyze the distribution patterns of robot errors and leverage spatial similarity for error prediction and compensation. However, existing methods in Cartesian space struggle to achieve accurate error estimation when the robot is loaded or the end-effector orientations are varied. To address these challenges, a novel method for robot error prediction and accuracy compensation within configuration space is proposed. The analysis of robot error distribution reveals that the spatial similarity of robot errors is more pronounced and stable in configuration space compared to Cartesian space, and this property exhibits significant anisotropy across joint dimensions. A spatial-interpolation-based unbiased estimation method with joint weights optimization is proposed for robot errors prediction, and the particle filter method is utilized to search for the optimal joint weights, enhancing the anisotropic characteristics of the prediction model. Based on the robot error prediction model, a cyclic searching method is employed to directly compensate for the joint angles. An experimental system is established using an industrial robot equipped with a 120 kg end-effector and a laser tracker. Eighty sampling points with diverse poses are randomly selected within the task workspace to measure the robot errors before and after compensation. The proposed method achieves an error prediction accuracy of 0.172 mm, reducing the robot error from the original 4.96 mm to 0.28 mm, thus meeting the stringent accuracy requirements for hole machining in robotic aerospace assembly processes.