Influence and experiment of cable-net manufacturing errors on surface accuracy of mesh reflector antennas

Cable-net structures are of substantial importance in the construction of large mesh reflector antennas.Owing to the inevitable errors in their manufacturing process,the reflector surface accuracy deteriorates.This study makes a comprehensive investigation of random manufacturing errors during constructing the mesh reflector antennas,and analyze its influence on reflector surface accuracy.Firstly,the sensitivity of reflector surface accuracy with respect to the random errors of the unstressed cable length is mathematically deducted.Secondly,a non-button connecting method is proposed and analyzed to reduce manufacturing errors.Thirdly,two physical experiment models based on 2.62-meter mesh reflector antenna are made.Finally,numerical examples and experimental tests are given to demonstrate the effectiveness of the proposed method.Compared with the traditional method,the proposed method can effectively reduce the influence of the manufacturing errors on the reflector surface accuracy.Moreover,the reduction in the sizes of the nodes also reduces the risk of entanglement of the mesh reflector antenna during the deployment process,and thereby improves the deployment reliability.

Surface accuracy optimization of mechanical parts with multiple circular holes for additive manufacturing based on triangular fuzzy number

Surface accuracy directly affects the surface quality and performance of mechanical parts.Circular hole,especially spatial non-planar hole set is the typical feature and working surface of mechanical parts.Compared with traditional machining methods,additive manufacturing(AM)technology can decrease the surface accuracy errors of circular holes during fabrication.However,an accuracy error may still exist on the surface of circular holes fabricated by AM due to the influence of staircase effect.This study proposes a surface accuracy optimization approach for mechanical parts with multiple circular holes for AM based on triangular fuzzy number(TFN).First,the feature lines on the manifold mesh are extracted using the dihedral angle method and normal tensor voting to detect the circular holes.Second,the optimal AM part build orientation is determined using the genetic algorithm to optimize the surface accuracy of the circular holes by minimizing the weighted volumetric error of the part.Third,the corresponding weights of the circular holes are calculated with the TFN analytic hierarchy process in accordance with the surface accuracy requirements.Lastly,an improved adaptive slicing algorithm is utilized to reduce the entire build time while maintaining the forming surface accuracy of the circular holes using digital twins via virtual printing.The effectiveness of the proposed approach is experimentally validated using two mechanical models.

Study on Wheel and Workpiece Position Error Influence on Surface Shape Accuracy during Ceramic Ball Grinding

The reasons for bringing surface accuracy error in ultra-precision grinding ceramic ball joint were analyzed,and the influences wheel position error and shaft run-out error on the ball joint surface accuracy were discussed.Through establishing three-dimensional grinding model,the mathematical relationship between the position error and surface accuracy was derived,and the distance from any point on spherical surface to the ideal center was calculated when position error existed,and a precise surface shape was got,and theoretical support was provided to improve the surface accuracy during the grinding process.Using self-developed ultraprecision grinding machine to do the ceramic ball grinding experiment,the surface accuracy PV value of ceramic spherical joint is 4.8μm.

Effect of Facet Displacement on Radiation Field and Its Application for Panel Adjustment of Large Reflector Antenna

Large reflector antennas are widely used in radars, satellite communication, radio astronomy, and so on. The rapid developments in these fields have created demands for development of better performance and higher surface accuracy. However, low accuracy and low effi- ciency are the common disadvantages for traditional panel alignment and adjustment. In order to improve the surface accuracy of large reflector antenna, a new method is pre- sented to determinate panel adjustment values from far field pattern. Based on the method of Physical Optics （PO）, the effect of panel facet displacement on radiation field value is derived. Then the linear system is constructed between panel adjustment vector and far field pattern. Using the method of Singular Value Decomposition （SVD）, the adjustment value for all panel adjustors are obtained by solving the linear equations. An experiment is conducted on a 3.7 m reflector antenna with 12 segmented panels. The results of simulation and test are similar, which shows that the presented method is feasible. Moreover, thediscussion about validation shows that the method can be used for many cases of reflector shape. The proposed research provides the instruction to adjust surface panels efficiently and accurately.

Exact mesh shape design of large cable-network antenna reflectors with flexible ring truss supports

An exact-designed mesh shape with favorable surface accuracy is of practical significance to the performance of large cable-network antenna reflectors. In this study, a novel design approach that could guide the generation of exact spatial parabolic mesh configurations of such reflector was proposed. By incorporating the traditional force density method with the standard finite element method, this proposed approach had taken the deformation effects of flexible ring truss supports into consideration, and searched for the desired mesh shapes that can satisfy the requirement that all the free nodes are exactly located on the objective paraboloid. Compared with the conventional design method,a remarkable improvement of surface accuracy in the obtained mesh shapes had been demonstrated by numerical examples. The present work would provide a helpful technical reference for the mesh shape design of such cable-network antenna reflector in engineering practice.

Development of a surface modeling method for mapping soil properties

High accuracy surface modeling （HASM） is a method which can be applied to soil property interpolation. In this paper, we present a method of HASM combined geographic information for soil property interpolation （HASM-SP） to improve the accuracy. Based on soil types, land use types and parent rocks, HASM-SP was applied to interpolate soil available P, Li, pH, alkali-hydrolyzable N, total K and Cr in a typical red soil hilly region. To evaluate the performance of HASM-SP, we compared its performance with that of ordinary kriging （OK）, ordinary kriging combined geographic information （OK-Geo） and stratified kriging （SK）. The results showed that the methods combined with geographic information including HASM-SP and OK-Geo obtained a lower estimation bias. HASM-SP also showed less MAEs and RMSEs when it was compared with the other three methods （OK-Geo, OK and SK）. Much more details were presented in the HASM-SP maps for soil properties due to the combination of different types of geographic information which gave abrupt boundary for the spatial varia- tion of soil properties. Therefore, HASM-SP can not only reduce prediction errors but also can be accordant with the distribution of geographic information, which make the spatial simula- tion of soil property more reasonable. HASM-SP has not only enriched the theory of high accuracy surface modeling of soil property, but also provided a scientific method for the ap- plication in resource management and environment planning.

Hybrid-panel-based new design idea for large reflector antenna

Large reflector antennas are widely used as radio telescopes and active main reflectors are generally applied to improve the surface accuracy. Considering that the high cost has been one important problem in engineering, it is worth discussing whether it is necessary to install actuators on all the panels. Thus, in this paper, a hybrid-panel-based new design idea for large reflector antenna is proposed. Assuming that the actuators are installed only in the region of the reflector with large deformations and there are no actuators in other region to reduce the actuator number, the surface accuracies and the corresponding electromagnetic(EM) performances calculated by three different panel adjustment strategies are compared. The most effective method is that the deformed reflector should be first preadjusted to reduce the gravity deformation and then the panels equipped with actuators should be adjusted to the locations determined by the best fitting reflector(BFR) derived by the deformed reflector with no actuators. A 35 m reflector antenna is adopted as an example to calculate the surface accuracy and EM performance when parts of the panels are equipped with actuators. The simulation results show that there is no need to install actuators on all panels and the presented method can greatly reduce the number of actuators with guaranteed surface accuracy. Thus, during the antenna structural design phase, once the surface accuracy requirement is given, the number of actuators can be minimized to reduce the manufacturing and maintenance costs as much as possible. This paper can provide valuable guidance for the design of an active main reflector with hybrid panels.

HASM quantum machine learning

The miniaturization of transistors led to advances in computers mainly to speed up their computation.Such miniaturization has approached its fundamental limits.However,many practices require better computational resources than the capabilities of existing computers.Fortunately,the development of quantum computing brings light to solve this problem.We briefly review the history of quantum computing and highlight some of its advanced achievements.Based on current studies,the Quantum Computing Advantage(QCA)seems indisputable.The challenge is how to actualize the practical quantum advantage(PQA).It is clear that machine learning can help with this task.The method used for high accuracy surface modelling(HASM)incorporates reinforced machine learning.It can be transformed into a large sparse linear system and combined with the Harrow-Hassidim-Lloyd(HHL)quantum algorithm to support quantum machine learning.HASM has been successfully used with classical computers to conduct spatial interpolation,upscaling,downscaling,data fusion and model-data assimilation of ecoenvironmental surfaces.Furthermore,a training experiment on a supercomputer indicates that our HASM-HHL quantum computing approach has a similar accuracy to classical HASM and can realize exponential acceleration over the classical algorithms.A universal platform for hybrid classical-quantum computing would be an obvious next step along with further work to improve the approach because of the many known limitations of the HHL algorithm.In addition,HASM quantum machine learning might be improved by:(1)considerably reducing the number of gates required for operating HASM-HHL;(2)evaluating cost and benchmark problems of quantum machine learning;(3)comparing the performance of the quantum and classical algorithms to clarify their advantages and disadvantages in terms of accuracy and computational speed;and(4)the algorithms would be added to a cloud platform to support applications and gather active feedback from users of the algorithms.

Progress on spatial prediction methods for soil particle-size fractions

Soil particle-size fractions(PSFs),including three components of sand,silt,and clay,are very improtant for the simulation of land-surface process and the evaluation of ecosystem services.Accurate spatial prediction of soil PSFs can help better understand the simulation processes of these models.Because soil PSFs are compositional data,there are some special demands such as the constant sum(1 or 100%) in the interpolation process.In addition,the performance of spatial prediction methods can mostly affect the accuracy of the spatial distributions.Here,we proposed a framework for the spatial prediction of soil PSFs.It included log-ratio transformation methods of soil PSFs(additive log-ratio,centered log-ratio,symmetry log-ratio,and isometric log-ratio methods),interpolation methods(geostatistical methods,regression models,and machine learning models),validation methods(probability sampling,data splitting,and cross-validation) and indices of accuracy assessments in soil PSF interpolation and soil texture classification(rank correlation coefficient,mean error,root mean square error,mean absolute error,coefficient of determination,Aitchison distance,standardized residual sum of squares,overall accuracy,Kappa coefficient,and Precision-Recall curve) and uncertainty analysis indices(prediction and confidence intervals,standard deviation,and confusion index).Moreover,we summarized several paths on improving the accuracy of soil PSF interpolation,such as improving data distribution through effective data transformation,choosing appropriate prediction methods according to the data distribution,combining auxiliary variables to improve mapping accuracy and distribution rationality,improving interpolation accuracy using hybrid models,and developing multi-component joint models.In the future,we should pay more attention to the principles and mechanisms of data transformation,joint simulation models and high accuracy surface modeling methods for multi-components,as well as the combination of soil particle size curves with stochastic simulations.We proposed a clear framework for improving the performance of the prediction methods for soil PSFs,which can be referenced by other researchers in digital soil sciences.