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This paper aims to provide a shimming method based on scanned data and finite element analysis (FEA) for a wing box assembly involving non-uniform gaps. The effort of the present work is to deal with gap compensation problem using hybrid shims composed of solid and liquid forms.

First, the assembly gaps of the mating components are calculated based on the scanned surfaces. The local gap region is extracted by the seed point and region growth algorithm from the scattered point cloud. Second, with the constraints of hole margin, gap space and shim specification, the optional shimming schemes are designed by the exhaustive searching method. Finally, the three-dimensional model of the real component is reconstructed based on the reverse engineering techniques, such as section lines and sweeping. Using FEA software ABAQUS, the stress distribution and damage status of the joints under tensile load are obtained for optimal scheme selection.

With the scanned mating surfaces, the non-uniform gaps are digitally evaluated with accurate measurement and good visualization. By filling the hybrid shims in the assembly gaps, the joint structures possess similar load capacity but stronger initial stiffness compared to the custom-shimmed structures.

This method has been tested with the interface data of a wing tip, and the results have shown good efficiency and automation of the shimming process.

The proposed method can decrease the manufacturing cost of shims, shorten the shimming process cycle and improve the assembly efficiency.

The purpose of this paper is to introduce a robotic boring system for intersection holes in aircraft assembly. The system is designed to improve the boring quality and position accuracy of the intersection holes.

To improve the boring quality of intersection holes, a robot posture optimization model is established. The target of the model is to maximize the robot stiffness and the variate is location of the robot on the guideway. The model is solved by the iterative IKP algorithm based on the Jacobian matrix. To improve the position accuracy of intersection holes, a robot positioning accuracy compensation method is introduced. In the method, a laser tracker is used to measure the actual position and orientation of the boring bar. Combined with the desired position and orientation, the error can be obtained and compensated.

In practical case of the robotic boring system, the robot stiffness is effectively improved and the surface roughness of intersection holes achieves a grade of Ra0.8. Besides, the robot end achieves a position accuracy of 0.05 mm and an orientation accuracy of 0.05°.

The robotic boring system has been applied successfully in one of the aircraft assembly projects in northwest China.

The robotic boring system can be applied for machining intersection holes in an aircraft assembly. With the robot posture optimization method and accuracy compensation method, the boring quality and position accuracy of the intersection holes can be guaranteed.

Aircraft assembly demands high position accuracy of drilled fastener holes. Automated drilling is a key technology to fulfill the requirement. The purpose of the paper is to conduct positioning variation analysis and control for an automated drilling to achieve a high positioning accuracy.

The nominal and varied connective models of automated drilling are constructed for positioning variation analysis regarding automated drilling. The principle of a strategy for reducing positioning variation in drilling, which shortens the positioning variation chain with the aid of an industrial camera-based vision system, is explored. Moreover, other strategies for positioning variation control are developed based on mathematical analysis to further reduce the position errors of the drilled fastener holes.

The propagation and accumulation of an automated drilling system’s positioning variation are explored. The principle of reducing positioning variation in an automated drilling using a monocular vision system is discussed from the view of variation chain.

The strategies for reducing positioning variation, rooted in the constructed positioning variation models, have been applied to a machine-tool based automated drilling system. The system is developed for a wing assembly of an aircraft in the Aviation Industry Corporation of China.

Propagation, accumulation and control of positioning variation in an automated drilling are comprehensively explored. Based on this, the positioning accuracy in an automated drilling is controlled below 0.13 mm, which can meet the requirement for the assembly of the aircraft.

In a dual-robot system, the relative position error is a superposition of errors from each mono-robot, resulting in deteriorated coordination accuracy. This study aims to enhance relative accuracy of the dual-robot system through direct compensation of relative errors. To achieve this, a novel calibration-driven transfer learning method is proposed for relative error prediction in dual-robot systems.

A novel local product of exponential (POE) model with minimal parameters is proposed for error modeling. And a two-step method is presented to identify both geometric and nongeometric parameters for the mono-robots. Using the identified parameters, two calibrated models are established and combined as one dual-robot model, generating error data between the nominal and calibrated models’ outputs. Subsequently, the calibration-driven transfer, involving pretraining a neural network with sufficient generated error data and fine-tuning with a small measured data set, is introduced, enabling knowledge transfer and thereby obtaining a high-precision relative error predictor.

Experimental validation is conducted, and the results demonstrate that the proposed method has reduced the maximum and average relative errors by 45.1% and 30.6% compared with the calibrated model, yielding the values of 0.594 mm and 0.255 mm, respectively.

First, the proposed calibration-driven transfer method innovatively adopts the calibrated model as a data generator to address the issue of real data scarcity. It achieves high-accuracy relative error prediction with only a small measured data set, significantly enhancing error compensation efficiency. Second, the proposed local POE model achieves model minimality without the need for complex redundant parameter partitioning operations, ensuring stability and robustness in parameter identification.

This paper aims to propose a calibration model for kinematic parameters identification of serial robot to improve its positioning accuracy, which only requires position measurement of the end-effector.

The proposed model is established based on local frame representation of the product of exponentials (local POE) formula, which integrates all kinematic errors into the twist coordinates errors; then they are identified with the tool frame’ position deviations simultaneously by an iterative least squares algorithm.

To verify the effectiveness of the proposed method, extensive simulations and calibration experiments have been conducted on a 4DOF SCARA robot and a 5DOF drilling machine, respectively. The results indicate that the proposed model outperforms the existing model in convergence, accuracy, robustness and efficiency; fewer measurements are needed to gain an acceptable identification result.

This calibration method has been applied to a variable-radius circumferential drilling machine. The machine’s positioning accuracy can be significantly improved from 11.153 initially to 0.301 mm, which is well in the tolerance (±0.5 mm) for fastener hole drilling in aircraft assembly.

An accurate and efficient kinematic calibration model has been proposed, which satisfies the completeness, continuity and minimality requirements. Due to generality, this model can be widely used for serial robot kinematic calibration with any combination of revolute and prismatic joints.

This paper aims to present error compensation based on surface reconstruction to improve the positioning accuracy of industrial robots.

In previous research, it has been proved that the positioning error of industrial robots is continuous on the two-dimensional manifold of six-joint space. The point cloud generated by positioning error data can be used to fit the continuous surfaces, which makes it possible to apply surface reconstruction on error compensation. The moving least-squares interpolation and the B-spline method are used for the error surface reconstruction.

The results of experiments and simulations validate the effectiveness of error compensation by the moving least-squares interpolation and the B-spline method.

The proposed methods can control the average of compensated positioning error within 0.2 mm, which meets the requirement of a tolerance (±0.5 mm) for fastener hole drilling in aircraft assembly.

The error surface reconstruction based on the B-spline method has great superiority because fewer sample points are needed to use this method than others while keeping the compensation accuracy at the same level. The control points of the B-spline error surface can be adjusted with measured data, which can be applied for the error prediction in any temperature field.

The riveting process is a metal forming process involving complex elastic-plastic deformation, which will induce a compressive residual stress field and cause local distortions in the connecting areas. Regarding to the aircraft panel assemblies with plenty of rivets, the global deformation is inevitable and undesired, leading difficulties to downstream assembly processes. This paper aims to present a new method for the local distortion calculation and the global deformation prediction of sheet panel assemblies during the automated riveting process.

In this paper, a simplified algebraic study is presented to analyze the local distortion of single countersunk rivet joint with the consideration of the barrel-like shape of the driven head and the through-thickness variations along the rivet shank. Then, an equivalent rivet unit is proposed based on the result of the algebraic study and embedded into the global-level model for the prediction of the overall distortions of riveted panels.

The algebraic study is able to reach a more precise contour of the deformed rivet than the traditional assumption of cylindrical deformations and rapidly determine the equivalent coefficients of the riveting unit. The result also shows an industrial acceptable accuracy of the prediction for the global deformations of the double-layered panel assemblies widely used in the aircraft panel structures.

A new local-global method for predicting the deformations of the riveted panel assembly based on the algebraic study of the local distortions is proposed to help the engineers in the early design stages or in the assembly process planning stage.

To gain accurate support for large aircraft structures by ball joints in aircraft digital assembly, this paper aims to propose a novel approach based on visual servoing such that the positioner’s ball-socket can automatically and adaptively approach the ball-head fixed on the aircraft structures.

Image moments of circular marker labeled on the ball-head are selected as visual features to control the three translational degrees of freedom (DOFs) of the positioner, where the composite Jacobian matrix is full rank. Kalman–Bucy filter is adopted for its online estimation, which makes the control scheme more flexible without system calibration. A combination of proportional control with sliding mode control is proposed to improve the system stability and compensate uncertainties of the system.

The ball-socket can accurately and smoothly reach its desired position in a finite time (50 s). Positional deviations between the spherical centers of ball-head and ball-socket in the X-Y plane can be controlled within 0.05 mm which meets the design requirement.

The proposed approach has been integrated into the pose alignment system. It has shown great potential to be widely applied in the leading support for large aircraft structures in aircraft digital assembly.

An adaptive approach for accurate support of large aircraft structures is proposed, which possesses characteristics of high precision, high efficiency and excellent stability.

For the automatic drilling and riveting in panel assembly, gaps between the skin and strangers are inevitable and undesirable. At present, the determination of pre-joining schemes relies on workers’ experience, introducing excessive number and inappropriate locations of pre-joining. This paper aims to present a new method for the evaluation of residual clearances after pre-joining and the pre-joining scheme optimization, providing operation guidance for the workers in panel assembly workshop.

In this paper, an equivalent gap assembly model for pre-joining is proposed on the basis of the mechanism of variation. This model retains the essential elastic behavior of the key features during the pre-joining operation and calculates the residual clearances in the view of the potential energy. Subsequently, this method is embedded into a Pareto optimality-based genetic algorithm, and the optimal pre-joining schemes are achieved with the consideration of the total residual clearances and the permissive tolerances.

The equivalent gap assembly model has the capability to predict an acceptable degree of accuracy of the residual clearances and achieve the optimized pre-joining schemes with less number of pre-joining at the same level of residual clearances.

The optimized pre-joining schemes are given in the form of Pareto optimality set, and workers can select suitable results according to their inclination to the quality and efficiency.

The paper is the first to propose the equivalent gap assembly model for the pre-joining operation, which provides for the simplification of the calculation of residual clearances based on the constrained variation principles.

Assembly variation analysis generally demands probability distributions of variation sources. However, due to small production volume in aircraft manufacturing, especially prototype manufacturing, the probability distributions are hard to obtain, and only the small-sample data of variation sources can be consulted. Thus, this paper aims to propose a variation analysis method driven by small-sample data for compliant aero-structure assembly.

First, a hybrid assembly variation model, integrating rigid effects with flexibility, is constructed based on the homogeneous transformation and elasticity mechanics. Then, the bootstrap approach is introduced to estimate a variation source based on small-sample data. The influences of bootstrap parameters on the estimation accuracy are analyzed to select suitable parameters for acceptable estimation performance. Finally, the process of assembly variation analysis driven by small-sample data is demonstrated.

A variation analysis method driven by small-sample data, considering both rigid effects and flexibility, is proposed for aero-structure assembly. The method provides a good complement to traditional variation analysis methods based on probability distributions of variation sources.

With the proposed method, even if probability distribution information of variation sources cannot be obtained, accurate estimation of the assembly variation could be achieved. The method is well suited for aircraft assembly, especially in the stage of prototype manufacturing.

A variation analysis method driven by small-sample data is proposed for aero-structure assembly, which can be extended to deal with other similar applications.