Ultrasonic Impact Treatment (UIT) is an effective technique for surface refinement and residual stress reduction, which is widely used in welding. This study investigates UIT-assisted Wire and Arc Additive manufacturing (WAAM). The residual stress, grain morphology and mechanical properties of post-UIT and as-deposited samples are studied. The result demonstrates that the UIT has a significant influence on the decrease of the residual stress. Moreover, the residual stress of the post-UIT samples is much lower than that of the as-deposited samples. The samples fabricated by UIT-assisted WAAM have a novel, bamboo-like distribution of prior-β grains, an alternating distribution of short columnar grains and equiaxed grains. The grain size of this bamboo-like structure is much smaller than the coarsen columnar grains. In addition, the mechanical properties of the post-UIT and as-deposited samples are compared. The results indicate that the average tensile strength of the post-UIT samples is higher, while the average elongation of the post-UIT samples is lower.

Geometric characteristics provide an important means for characterization of the quality of direct laser deposition. Therefore, improving the accuracy of a prediction model is helpful for improving deposition efficiency and quality. The three main input variables are laser power, scanning speed, and powder-feeding rate, while the width and height of the melt track are used as outputs. By applying a multi-output support vector regression (M-SVR) model based on a radial basis function (RBF), a non-linear model for predicting the geometric features of the melt track is developed. An orthogonal experimental design is used to conduct the experiments, the results of which are chosen randomly as training and testing data sets. On the one hand, compared with single-output support vector regression (S-SVR) modeling, this method reduces the root mean square error of height prediction by 22%, with faster training speed and higher prediction accuracy. On the other hand, compared with a backpropagation (BP) neural network, the average absolute error in width is reduced by 5.5%, with smaller average absolute error and better generalization performance. Therefore, the established model can provide a reference to select direct laser deposition parameters precisely and can improve the deposition efficiency and quality.

The propagation of variations, such as fixture errors and datum errors resulting from assembly and machining processes, has been extensively studied. However, only a few studies that focus on form error propagation in assembly systems have been implemented. Machining errors, especially form errors, have great impact on assembly accuracy and accuracy stability of precision mechanical systems. With form errors being the research object, a method for calculating mating variation and specifying mating coordinate is proposed to improve the accuracy of the variation propagation model. Taking into account the form error of mating surfaces, the assembly variation propagation of a precision mechanical system is analyzed, and the brief derivation procedure of the variation propagation model is introduced afterwards. The variation propagation model involves a new concept of mating variation specified by the two mating surfaces. An innovative method, the difference surface search based method, is proposed to calculate the mating variation amongst the mating surfaces. The obtained mating variation is then utilized to specify the mating coordinate in the variation propagation model. Moreover, FEM is employed to simulate the contact state of the two mating surfaces to demonstrate effectiveness of the proposed method. Meanwhile, the mating variation and mating coordinate obtained are incorporated into the assembly variation propagation model, which is then verified by a following case study through a comparison between the calculated results and the experimental results. The comparing results indicate that the established model improves the prediction of assembly accuracy. The developed model enables the investigation of various fundamental issues in variation reduction, including variation analysis, process monitoring, accuracy prediction, and accuracy control.

The minimum zone method is currently a traditional means for evaluating form error. However, it cannot reveal the effects of form error on the scale of assembly accuracy. To address this deficiency, however, considering the propagation of geometric error in assembly process, this article proposed an entropy-based evaluation method for plane form error for precision mechanical system, which includes primary evaluation, convex hull search, top plane establishment, local entropy analysis and obtaining comprehensive evaluation index. Experimental results demonstrate that the proposed method can evaluate the plane form distribution error, which suggests the assemblability of parts, with the same form errors accurately and stably.

Hemispherical resonator gyro (HRG) is a high-precision gyro developed on the traditional mechanical gyro and optical gyro. The precise assembly of the resonator and the electrode base is a key technology to ensure the working performance of the HRG. In this paper, a method for identifying the assembly pose error of HRG with flat electrode is proposed. Firstly, the specific definition and quantitative description of the assembly pose error of the resonator were given, and the mathematical model between the assembly pose error and the equivalent capacitance was established. Then, a finite element simulation model between assembly pose error and equivalent capacitance was established in COMSOL Multiphysics, which verified the mathematical model. In addition, on the basis of the mathematical model, the influence of the assembly error on the equivalent capacitance was explored, and the sensitivity of the assembly movement error and rotation error to the equivalent capacitance was analyzed. Finally, the nonlinear relationship between the equivalent capacitance and the assembly pose error is established based on the support vector regression (SVR) model, therefore the assembly pose error identification was efficiently and reliably realized. The method establishes the quantitative relationship between the assembly pose error and the equivalent capacitance, and realizes the accurate and rapid identification of the assembly pose error, which provides a theoretical basis and realization method for the precise assembly process of the high-precision HRG.