Design and Accuracy Analysis of a Metamorphic CNC Flame Cutting Machine for Ship Manufacturing

The current research of processing large size fabrication holes on complex spatial curved surface mainly focuses on the CNC flame cutting machines design for ship hull of ship manufacturing. However, the existing machines cannot meet the continuous cutting requirements with variable pass conditions through their fixed configuration, and cannot realize high-precision processing as the accuracy theory is not studied adequately. This paper deals with structure design and accuracy prediction technology of novel machine tools for solving the problem of continuous and high-precision cutting. The needed variable trajectory and variable pose kinematic characteristics of non-contact cutting tool are figured out and a metamorphic CNC flame cutting machine designed through metamorphic principle is presented. To analyze kinematic accuracy of the machine, models of joint clearances, manufacturing tolerances and errors in the input variables and error models considering the combined effects are derived based on screw theory after establishing ideal kinematic models. Numerical simulations, processing experiment and trajectory tracking experiment are conducted relative to an eccentric hole with bevels on cylindrical surface respectively. The results of cutting pass contour and kinematic error interval which the position error is from -0.975 mm to ＋0.628 mm and orientation error is from -0.01 rad to ＋0.01 rad indicate that the developed machine can complete cutting process continuously and effectively, and the established kinematic error models are effective although the interval is within a ＇large＇ range. It also shows the matching property between metamorphic principle and variable working tasks, and the mapping correlation between original designing parameters and kinematic errors of machines. This research develops a metamorphic CNC flame cutting machine and establishes kinematic error models for accuracy analysis of machine tools.

Workspace and Accuracy Analysis on a Novel 6‑UCU Bone‑attached Parallel Manipulator

With the increasingly more extensive application of the medical surgical robot in the clinic,higher requirements have been put forward for medical robots.The bone-attached robot,a popular orthopedic robot in recent years,has a tendency of miniaturization and refinement.Thus,a bone-attached parallel manipulator(PM)based on 6-UCU(universalcylindrical-universal)configuration is proposed,which is characterized by small volume,compact structure,high precision and six-dimensional force feedback.To optimize the structure and make it more compact,the workspace of the 6-UCU PM is analyzed based on the analysis of three kinds of constraint,and workspace model is established through spherical coordinate search method.This study also analyzes the influence of structural parameters on workspace,which may contribute to improving the efficiency of design and ensuring small-sized robots possess relatively large workspace.Moreover,to improve the motion accuracy,an error modeling method is developed based on the structure of 6-UCU PMs.According to this established error model,the output pose error curves are drawn using MATLAB software when the structure parameters change,and the influence of the structure and pose parameters change on the output pose error of PMs is analyzed.The proposed research provides the instruction to design and analysis of small PMs such as bone-attached robots.

Accuracy Analysis for 6-DOF PKM with Sobol Sequence Based Quasi Monte Carlo Method

To improve the precisions of pose error analysis for 6-dof parallel kinematic mechanism( PKM)during assembly quality control,a Sobol sequence based on Quasi Monte Carlo( QMC) method is introduced and implemented in pose accuracy analysis for the PKM in this paper. The Sobol sequence based on Quasi Monte Carlo with the regularity and uniformity of samples in high dimensions,can prevail traditional Monte Carlo method with up to 98. 59% and 98. 25% enhancement for computational precision of pose error statistics.Then a PKM tolerance design system integrating this method is developed and with it pose error distributions of the PKM within a prescribed workspace are finally obtained and analyzed.

Analysis of Robot Accuracy Assessed via its Joint Clearance by Virtual Prototyping Method

This paper discusses how joint clearance influences robot end effectorpositioning accuracy and a robot accuracy analysis approach based on a virtual prototype isproposed. First, a 5-DOF(Degree of freedom) neurosurgery robot was introduced. Then we built itsvirtual prototype, made movement planning and measured the manipulator tip accuracy, through whichthis robot accuracy portrait was obtained. Finally, in order to validate the robot accuracyanalysis approach which is based on a virtual prototype, the result was compared with that from amodel built by robot forward kinematics and robot differential kinematics. The robot accuracyanalysis approach presented in this paper gives a new way to enhance robot design quality , and helpto optimize the control and programming of the robot.

Parameter accuracy analysis of weak-value amplification process in the presence of noise

We theoretically introduce the statistical uncertainty of photon number and phase error to discuss the precision of parameters to be measured based on weak measurements. When the photon counting scheme is used, we discuss the relative accuracy of the system in the presence of phase error by using the orthogonal and nonorthogonal pre-and postselected states, respectively. When using the measurement scheme of pointer shift, we discuss the measurement accuracy in the presence of phase error, pointer resolution, and statistical uncertainty. These results give a guide way to get the smallest relative precision and deepen our understanding about weak measurement.

A predictive model for regional zenith tropospheric delay correction

The conventional zenith tropospheric delay(ZTD)model(known as the Saastamoinen model)does not consider seasonal variations affecting the delay,giving it low accuracy and stability.This may be improved with adjustments to account for annual and semi-annual variations.This method uses ZTD data provided by the Global Geodetic Observing System to analyze seasonal variations in the bias of the Saastamoinen model in Asia,and then constructs a model with seasonal variation corrections,denoted as SSA.To overcome the dependence of the model on in-situ meteorological parameters,the SSA+GPT3 model is formed by combining the SSA and GPT3(global pressure-temperature)models.The results show that the introduction of annual and semi-annual variations can substantially improve the Saastamoinen model,yielding small and time-stable variations in bias and root mean square(RMS).In summer and autumn,the bias and RMS are noticeably smaller than those from the Saastamoinen model.In addition,the SSA model performs better in low-latitude and low-altitude areas,and bias and RMS decease with the increase of latitude or altitude.The prediction accuracy of the SSA model is also evaluated for external consistency.The results show that the accuracy of the SSA model(bias:-0.38 cm,RMS:4.43 cm)is better than that of the Saastamoinen model(bias:1.45 cm,RMS:5.16 cm).The proposed method has strong applicability and can therefore be used for predictive ZTD correction across Asia.

Separation of Comprehensive Geometrical Errors of a 3-DOF Parallel Manipulator Based on Jacobian Matrix and Its Sensitivity Analysis with Monte-Carlo Method

Parallel kinematic machines （PKMs） have the advantages of a compact structure,high stiffness,a low moving inertia,and a high load/weight ratio.PKMs have been intensively studied since the 1980s,and are still attracting much attention.Compared with extensive researches focus on their type/dimensional synthesis,kinematic/dynamic analyses,the error modeling and separation issues in PKMs are not studied adequately,which is one of the most important obstacles in its commercial applications widely.Taking a 3-PRS parallel manipulator as an example,this paper presents a separation method of source errors for 3-DOF parallel manipulator into the compensable and non-compensable errors effectively.The kinematic analysis of 3-PRS parallel manipulator leads to its six-dimension Jacobian matrix,which can be mapped into the Jacobian matrix of actuations and constraints,and then the compensable and non-compensable errors can be separated accordingly.The compensable errors can be compensated by the kinematic calibration,while the non-compensable errors may be adjusted by the manufacturing and assembling process.Followed by the influence of the latter,i.e.,the non-compensable errors,on the pose error of the moving platform through the sensitivity analysis with the aid of the Monte-Carlo method,meanwhile,the configurations of the manipulator are sought as the pose errors of the moving platform approaching their maximum.The compensable and non-compensable errors in limited-DOF parallel manipulators can be separated effectively by means of the Jacobian matrix of actuations and constraints,providing designers with an informative guideline to taking proper measures for enhancing the pose accuracy via component tolerancing and/or kinematic calibration,which can lay the foundation for the error distinguishment and compensation.

Analysis of the Quality of Daily DEM Generation with Geosynchronous InSAR

Up-to-date digital elevation model(DEM)products are essential in many fields such as hazards mitigation and urban management.Airborne and low-earth-orbit(LEO)space-borne interferometric synthetic aperture radar(InSAR)has been proven to be a valuable tool for DEM generation.However,given the limitations of cost and satellite repeat cycles,it is difficult to generate or update DEMs very frequently(e.g.,on a daily basis)for a very large area(e.g.,continental scale or greater).Geosynchronous synthetic aperture radar(GEOSAR)satellites fly in geostationary earth orbits,allowing them to observe the same ground area with a very short revisit time(daily or shorter).This offers great potential for the daily DEM generation that is desirable yet thus far impossible with space-borne sensors.In this work,we systematically analyze the quality of daily GEOSAR DEM.The results indicate that the accuracy of a daily GEOSAR DEM is generally much lower than what can be achieved with typical LEO synthetic aperture radar(SAR)sensors;therefore,it is important to develop techniques to mitigate the effects of errors in GEOSAR DEM generation.

Optimizing accuracy of a parabolic cylindrical deployable antenna mechanism based on stiffness analysis

Accuracy of the fitted surface is of great importance to the performance of deployable antennas utilized in space.This paper proposes a stiffness analysis based fitting accuracy optimization method for achieving the optimal parameters of the parabolic cylindrical deployable antenna mechanism.The stiffness matrix of the proposed cylindrical antenna mechanism is established by assembling the stiffness of beams and tension cables.Structural deformations of the mechanism are calculated where the tensioned cable is substituted by a 2-node truss element and an equivalent force acting on the joint.Consideration of the tensity of tension cables,namely tensioned or slack,is transformed into a typical linear complementarity problem.Comparison between structural deformations of the mechanism fixed at different points is performed.Sensitivities of the geometric and structural parameters on fitting accuracy are investigated.Influence of force of the driven cable on structural deformations of antenna operated in different orbits is conducted.A fitting optimization method is proposed to minimize the structural deformations subject to constraints on volume and mass.Simulation result shows that the fitting accuracy of the antenna mechanism is improved significantly through the optimization.The proposed method can be utilized for the optimal design of other deployable mechanisms constructed by joining rigid links.

Accuracy analysis of gradient reconstruction on isotropic unstructured meshes and its effects on inviscid flow simulation

The accuracy of gradient reconstruction methods on unstructured meshes is analyzed both mathematically and numerically.Mathematical derivations reveal that,for gradient reconstruction based on the Green-Gauss theorem(the GG methods),if the summation of first-and-lower-order terms does not counterbalance in the discretized integral process,which rarely occurs,second-order accurate approximation of face midpoint value is necessary to produce at least first-order accurate gradient.However,gradient reconstruction based on the least-squares approach(the LSQ methods)is at least first-order on arbitrary unstructured grids.Verifications are performed on typical isotropic grid stencils by analyzing the relationship between the discretization error of gradient reconstruction and the discretization error of the face midpoint value approximation of a given analytic function.Meanwhile,the numerical accuracy of gradient reconstruction methods is examined with grid convergence study on typical isotropic grids.Results verify the phenomenon of accuracy degradation for the GG methods when the face midpoint value condition is not satisfied.The LSQ methods are proved to be at least first-order on all tested isotropic grids.To study gradient accuracy effects on inviscid flow simulation,solution errors are quantified using the Method of Manufactured Solutions(MMS)which was validated before adoption by comparing with an exact solution case,i.e.,the 2-dimensional(2D)inviscid isentropic vortex.Numerical results demonstrate that the order of accuracy(OOA)of gradient reconstruction is crucial in determining the OOA of numerical solutions.Solution accuracy deteriorates seriously if gradient reconstruction does not reach first-order.

Accuracy analysis in CH4MODwetland in the simulation of CH4 emissions from Chinese wetlands

Process-based models have been widely used to simulate the CH4 budget in natural wetlands,but there are still large uncenainties in ihe simulation processes.Accuracy analysis of process-based models is important to evaluate the reliability of model estimates for different wetlands.In this study,we analyzed the three sources of the model bias from a process-based model(CH4MODwetland)when simulating CH4 emissions from different wetland sites and types in China.On a national scale,the root-mean-square error(RMSE)decreased from 70.6% at daily scale to 27.1% at annual/seasonal scale.On a national scale,more than 90% of the model errors were from random error.At most of the sites random error contributed more than 60% to the model errors,except the Guangzhou,Haikou,Wuliangsu lake and Yancheng Estuary sites,which showed lower RMSE values.The model simulated higher RMSE at the tidal marsh sites.The model had capability to simulate the daily and annual/seasonal CH4 emissions from all wetland types(model efficiency>0),and performed better for coastal wetlands than peatlands and marshes,with model efficiency values of 0.75 and 0.99,respectively.When applied the model at annual/seasonal scale for peatlands,marshes,and coastal wetlands,the RMSE decreased a lot.The study indicated that CH4MODwetland is more accurate at annual/seasonal scale than daily scale for different wetland types across China.The non-stochastic portion of CH4 emissions from peatlands,marshes and coastal wetlands can be well predicted by the model.

Wind speed inversion and in-orbit assessment of the imaging altimeter on Tiangong-2 space station

Imaging altimeter(IALT)is a new type of radar altimeter system.In contrast to the conventional nadir-looking altimeters,such as HY-2 A altimeter,Jason-1/2,and TOPEX/Poseidon,IALT observes the earth surface at low incident angles(2.5°–8°),so its swath is much wider and its spatial resolution is much higher than the previous altimeters.This paper presents a wind speed inversion method for the recently launched IALT onboard Tiangong-2 space station.Since the current calibration results of IALT do not agree well with the well-known wind geophysical model function at low incidence angles,a neural network is used to retrieve the ocean surface wind speed in this study.The wind speed inversion accuracy is evaluated by comparing with the ECMWF reanalysis wind speed,buoy wind speed,and in-situ ship measurements.The results show that the retrieved wind speed bias is about–0.21 m/s,and the root-mean-square(RMS)error is about 1.85 m/s.The wind speed accuracy of IALT meets the performance requirement.

Novel Design Method of COG Measurement System Via Supporting Reaction Method

Center of gravity(COG)is an important parameter of projectiles and rockets,for which supporting reaction method(or support reaction method)is an important COG measurement method.Based on this supporting reaction method a novel design method is proposed to determine the key design parameters of the COG measurement system.The method can quantitatively analyze the influence of the design parameters on the COG accuracy,in the measurement system designed with supporting reaction method.Using the principle of static balance,the error propagation theory,and the system accuracy analysis method,the equal-range required sensor precision(RSP)surface and non-equal-range required sensor pair precision(RSPP)adapted surface are adopted.The influence of random errors(like sensor accuracy and distance calibration accuracy)is analyzed.The selection strategy of equal-range and non-equal-range sensors is chosen,and then the recommended calibration accuracy values are obtained.For the quality detection accuracy of±0.6 kg and the axial COG detection accuracy of±1.5 mm,the RSP surface is drawn by the proposed method,and the force sensor with±0.23 kg detection accuracy is selected.The experimental verification meets the accuracy requirements and verifies the effectiveness of the proposed design method for the system parameters of the COG measurement equipment.

HIGH ACCURACY ANALYSIS OF ELLIPTIC EIGENVALUE PROBLEM FOR THE WILSON NONCONFORMING FINITE ELEMENT

In this paper, the Wilson nonconforming finite element is considered for solving elliptic eigenvalue problems. Based on an interpolation postprocessing, superconvergence estimates of both eigenfunction and eigenvalue are obtained.

Use of Hi-resolution data for evaluating accuracy of traffic volume counts collected by microwave sensors

Over the past few years, the Utah Department of Transportation has developed the signal performance metrics （SPMs） system to evaluate the performance of signalized in- tersections dynamically. This system currently provides data summaries for several per- formance measures, one of them being turning movement counts collected by microwave sensors. As this system became public, there was a need to evaluate the accuracy of the data placed on the SPMs. A large-scale data collection was carried out to meet this need. Vehicles in the Hi-resolution data from microwave sensors were matched with the vehicles by ground-truth volume count data. Matching vehicles from the microwave sensor data and the ground-truth data manually collected required significant effort, A spreadsheet- based data analysis procedure was developed to carry out the task. A mixed model analysis of variance was used to analyze the effects of the factors considered on turning volume count accuracy. The analysis found that approach volume level and number of approach lanes would have significant effect on the accuracy of turning volume counts but the location of the sensors did not significantly affect the accuracy of turning volume counts. In addition, it was found that the location of lanes in relation to the sensor did not significantly affect the accuracy of lane-by-lane volume counts. This indicated that accu- racy analysis could be performed by using total approach volumes without comparing specific turning counts, that is, left-turn, through and right-turn movements. In general, the accuracy of approach volume counts collected by microwave sensors were within the margin of error that traffic engineers could accept. The procedure taken to perform the analysis and a summary of accuracy of volume counts for the factor combinations considered are presented in this paper.

Error modeling and compensating of a novel 6-DOF aeroengine rotor docking equipment

In the docking process of aeroengine rotor parts,docking accuracy that indicates the gaps between the end faces is strictly required.A key issue is improving docking accuracy using automated docking equipment.In this paper,a systematic study is carried out on the error modeling and compensation of a novel six-degrees-of-freedom(6-DOF)docking equipment for aeroengine rotors.First,a new model for indicating the main indexes of docking accuracy is proposed.Then,the error model of a specially designed 6-DOF docking equipment is established based on a modified Denavit Hartenberg method with five parameters.Subsequently,two error compensation methods are proposed.Based on the above models,a docking accuracy simulation algorithm is proposed using the Monte Carlo method.Finally,verification experiments are conducted.The results show that,for the maximum values and standard deviations of the gaps between the rotor end-faces in the actual and target positions and attitudes,i.e.,main indexes that represent docking accuracy,the deviation rates between the simulation and experimental results are less than20%.The modeling methods have referential significance.The decline rates of these values are 50–65%when using the two proposed compensation methods.The compensation methods significantly improve the docking accuracy.

Estimation of particle depth from two defocused images using the Fourier transform

Depth from defocus is one technology for depth estimation.We estimate particle depth information from two defocused images captured simultaneously by two coaxial cameras with different imaging distances.The images are processed with the Fourier transform to obtain the characteristic parameter(i.e.,the standard deviation of the relative blur kernel of these two defocused images).First,we theoretically analyze the functional relationship between the object depth and the standard deviation or variation of the relative blur kernel.Then,we verify the relationship experimentally.We analyze the influence of particle size,window size and image noise on the calibration curves using both numerical simulations and experiments.We obtain the depth range and accuracy of this measurement system experimentally.For the verification experiments,we use a sample of glass microbeads and the irregularly-shaped dust particles on a microscope slide.Both of these experiments present a suitable depth measurement result.Finally,we apply the measuring system to the depth estimation of drops from a small anti-fogging spray.The results show that our system and image processing algorithm are robust for different types of particles,facilitating the in-line three-dimensional positioning of particles.

Prediction of effluent concentration in a wastewater treatment plant using machine learning models

Of growing amount of food waste, the integrated food waste and waste water treatment was regarded as one of the efficient modeling method. However, the load of food waste to the conventional waste treatment process might lead to the high concentration of total nitrogen（T-N） impact on the effluent water quality. The objective of this study is to establish two machine learning models-artificial neural networks（ANNs） and support vector machines（SVMs）, in order to predict 1-day interval T-N concentration of effluent from a wastewater treatment plant in Ulsan, Korea. Daily water quality data and meteorological data were used and the performance of both models was evaluated in terms of the coefficient of determination（R^2）, Nash-Sutcliff efficiency（NSE）, relative efficiency criteria（d rel）. Additionally, Latin-Hypercube one-factor-at-a-time（LH-OAT） and a pattern search algorithm were applied to sensitivity analysis and model parameter optimization, respectively. Results showed that both models could be effectively applied to the 1-day interval prediction of T-N concentration of effluent. SVM model showed a higher prediction accuracy in the training stage and similar result in the validation stage.However, the sensitivity analysis demonstrated that the ANN model was a superior model for 1-day interval T-N concentration prediction in terms of the cause-and-effect relationship between T-N concentration and modeling input values to integrated food waste and waste water treatment. This study suggested the efficient and robust nonlinear time-series modeling method for an early prediction of the water quality of integrated food waste and waste water treatment process.