Nano-cutting mechanism of ion implantation-modified SiC: reducing subsurface damage expansion and abrasive wear.

This study utilized ion implantation to modify the material properties of silicon carbide (SiC) to mitigate subsurface damage during SiC machining. The paper analyzed the mechanism of hydrogen ion implantation on the machining performance of SiC at the atomic scale. A molecular dynamics model of nanoscale cutting of an ion-implanted SiC workpiece using a non-rigid regular tetrakaidecahedral diamond abrasive grain was established. The study investigated the effects of ion implantation on crystal structure phase transformation, dislocation nucleation, and defect structure evolution. Results showed ion implantation modification decreased the extension depth of amorphous structures in the subsurface layer, thereby enhancing the surface and subsurface integrity of the SiC workpiece. Additionally, dislocation extension length and volume within the lattice structure were lower in the ion-implanted workpiece compared to non-implanted ones. Phase transformation, compressive pressure, and cutting stress of the lattice in the shear region per unit volume were lower in the ion-implanted workpiece than the non-implanted one. Taking the diamond abrasive grain as the research subject, the mechanism of grain wear under ion implantation was explored. Grain expansion, compression, and atomic volumetric strain wear rate were higher in the non-implanted workpiece versus implanted ones. Under shear extrusion of the SiC workpiece, dangling bonds of atoms in the diamond grain were unstable, resulting in graphitization of the diamond structure at elevated temperatures. This study established a solid theoretical and practical foundation for realizing non-destructive machining at the atomic scale, encompassing both theoretical principles and practical applications.

Surrogate-Model-Based Interval Analysis of Spherical Conformal Array Antenna with Power Pattern Tolerance.

A method based on an interval arithmetic is proposed to analyze uncertain factors such as the curvature radii, excitation amplitude, and excitation phase of a spherical conformal array antenna. An interval description of element factors under different curvature radii of spherical substrates is established using the surrogate model based on the data obtained through a full-wave analysis method. The interval formula of the spherical curvature radius and array element position error is derived and the effects of the spherical radius tolerance, excitation amplitude tolerance, and excitation phase tolerance on the antenna power pattern are studied. To evaluate the effectiveness and reliability of the proposed method, a set of representative numerical results are reported and discussed and a comparison with the Monte Carlo methods and full-wave simulation is described. This method can be widely used during the antenna design and before the antenna prototyping/manufacturing to predict the effects, on the radiation performance, of possible errors/tolerances in the antenna structure to guarantee the antenna working 'in operation'.

Investigation on SMT Product Defect Recognition Based on Multi-Source and Multi-Dimensional Data Reconstruction.

The recognition of defects in the solder paste printing process significantly influences the surface-mounted technology (SMT) production quality. However, defect recognition via inspection by a machine has poor accuracy, resulting in a need for the manual rechecking of many defects and a high production cost. In this study, we investigated SMT product defect recognition based on multi-source and multi-dimensional data reconstruction for the SMT production quality control process in order to address this issue. Firstly, the correlation between features and defects was enhanced by feature interaction, selection, and conversion. Then, a defect recognition model for the solder paste printing process was constructed based on feature reconstruction. Finally, the proposed model was validated on a SMT production dataset and compared with other methods. The results show that the accuracy of the proposed defect recognition model is 96.97%. Compared with four other methods, the proposed defect recognition model has higher accuracy and provides a new approach to improving the defect recognition rate in the SMT production quality control process.