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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.

The purpose of this paper is to reveal the pattern between government innovation funding and enterprise value creation. Many factors, including government innovation funding, R&D ability, corporate governance and some company characteristics significantly affected the efficiency of firm value creation.

This paper proposed a novel methodology based on clustering-rough sets to explore the characteristics of enterprise value creation behavior, and map the relationship between government innovation funding and enterprise value creation. The agglomerative hierarchical clustering (AHC) algorithm were used to classify firm performance and get two types of value creation efficiencies and to discretize condition attributes because the rough set theory cannot deal with continuous attributes. This paper utilized the rough sets method to realize data mining and get rules of government innovation funding and enterprise value creation.

R&D ability, proportion of independent directors, remuneration of directors, operating revenue, number of employees, price-earnings ratio, quick ratio, capital intensity and ROA were important to identify firm value creation efficiency when government funded the firms. Firms of high level of government innovation funding, high lagged R&D ratio, high remuneration of directors, low price-earnings ratio, low quick ratio, moderate capital intensity and high ROA were more likely to have high efficiency of value creation.

Since China implemented the innovation-driven development strategy, facilitating enterprise innovation has become an important way to achieve high-quality economic growth. With constantly increasing of Chinese government innovation funding, studying on the effect of government innovation funding on firm’s value creation is significant to improve the efficiency of government resource allocation. It is valuable to reveal the pattern between government innovation funding and enterprise value creation based on the value added theory. The rules obtained could be used to provide decision-making support to improve the efficiency of government innovation funding and prevent waste of government resources effectively.

With the advancement of social economy, science and technology, nowadays, people face increasingly complex decision-making problems and ever-growing decision-associated information contents. Owing to the unique advantages of the evidence theory, evidence decision flow fits in well with the cognitive process of human beings, which provides us with an effective decision method. However, traditional evidence theories are built upon the one-dimensional evidence recognition framework, which merely reflects the reliability of information determined by evidence source and as such may fail to present the characteristic information of evidence source itself or the information required for the process of determining reliability. This greatly influences processes in decision-making, such as evidence processing and combination. This essay aims to propose a two-dimensional evidence-reasoning theory to address specific decision problems.

This article uses a two-dimensional evidence framework to reflect the process and characteristic information of evidence source based on the traditional evidence framework. As a result, evidence is processed and combined by two-dimensional modified information.

This research is of theoretical and practical significance by extending theoretical connotation, fully utilizing precise evidence information and therefore meeting the requirements of efficient and accurate decision-making performances.

The theory adds a two-dimension to modify and capture evidence on the basis of the traditional evidence framework. This proposal has significant theoretical and practical value to expand evidence theory and provides more accurate use of evidence information, higher efficiency and quality requirements and more precise decision-making.

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.

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.

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.

This paper aims to propose a roadmap to control the robot–subassembly (R–S) coordination errors in movable robotic drilling. Fastener hole drilling for multi-station aircraft assembly demands a robotic drilling system with expanded working volume and high positioning accuracy. However, coordination errors often exist between the robot and the subassembly to be drilled because of disturbances.

Mechanical pre-locating and vision-based robot base frame calibration are consecutively implemented to achieve in-process robot relocation after station transfer. Thus, coordination errors induced by robotic platform movements, inconsistent thermal effects, etc. are eliminated. The two-dimensional (2D) vision system is applied to measure the remainder of the R–S coordination errors, which is used to enhance the positioning accuracy of the robot. Accurate estimation of measured positioning errors is of great significance for evaluating the positioning accuracy. For well estimation of the positioning errors with small samples, a bootstrap approach is put forward.

A roadmap for R–S coordination error control using a 2D vision system, composed of in-process relocation, coordination error measurement and drilled position correction, is developed for the movable robotic drilling.

The proposed roadmap has been integrated into a drilling system for the assembly of flight control surfaces of a transport aircraft in Aviation Industry Corporation of China. The position accuracy of the drilled fastener holes is well ensured.

A complete roadmap for controlling coordination errors and improving positioning accuracy is proposed, which makes the high accuracy and efficiency available in movable robotic drilling for aircraft manufacturing.

A large number of fastener holes have to be drilled with high quality in the circumferential splice region during the assembly of aircraft fuselage. The purpose of this paper is to design an automatic stepping mechanism for a circumferential splice drilling machine, to meet the requirements of large workspace and high accuracy in drilling at the same time.

A docking position detection method based on magnetic proximity sensors is proposed for the positioning of the arc-shaped rail with respect to the circumferential rails, which significantly improves the accuracy and reliability of automatic stepping. The slipping phenomenon of the end-effector is analyzed, and the optimized counter weights are used to eliminate the slipping and improve the working stability of the stepping mechanism.

An automatic stepping mechanism is developed for the circumferential splice drilling machine, which comprises the docking position detection method and the elimination/suppression method of the end-effector’s slipping.

The proposed automatic stepping mechanism has been integrated into the circumferential splice drilling machine for the fuselage assembly in an aircraft company in China.

An automatic stepping scheme for the circumferential splice drilling machine is proposed, which enhances the efficiency in circumferential splice drilling in aircraft fuselage assembly.

Knowledge sharing has been recognized as a positive force for the survival of an organization but the factors that promote or discourage knowledge sharing behaviors in the organizational context are poorly understood. The purpose of this paper is to enhance our collective understanding of the factors affecting knowledge sharing behaviors of knowledge workers in the organizational context.

Drawing from multiple streams of research including social psychology, organizational learning, knowledge management, information systems and so forth, this research developed an integrated theoretical model. The Theory of Planned Behavior model, motivation theory, social exchange theory, economic exchange theory and self determination theory were employed here. In total, 180 responses from ten companies in China were collected.

The theoretical model was validated within the context of a single empirical study. The findings provided significant statistical support for the research model, accounting for about 60 percent of the variance in the behavioral intention to share knowledge and 41 percent variance in the actual knowledge sharing behavior. In total, 11 of the 12 hypothesized relationships were supported.

This exploratory research attempted to fill the void in the integrative research by investigating the most crucial factors that influence the knowledge sharing behaviors of knowledge workers.