To enhance the digitalization level of shipbuilding, this study focuses on the construction methods and applications of digital shipyards. The design of different quantities and layouts of reference points is based on the dock's spatial dimensions, the principles of reference point arrangement, and the required measurement accuracy. An assembly coordinate system is established using the horizontal reference plane determined by the electronic level and the longitudinal movement direction of the assembly system equipment. A digital measurement process specification is developed based on the constructed spatial measurement field. Finally, using a specific ship structure model as an example, three typical application scenarios are introduced: segment assembly positioning measurement, total segment alignment positioning measurement, and structural internal equipment base positioning measurement. The construction of the spatial measurement field in the shipyard lays an important foundation for the subsequent creation of a digital shipyard.

Phased array ultrasonic testing (PAUT) is an advanced technique used for non-destructive testing (NDT) to detect and assess the integrity of materials or structures. In composite materials with significant acoustic attenuation, electrical noise significantly impacts PAUT detection accuracy, highlighting a crucial issue. This paper presents a comprehensive analysis of electrical noise, explains the relationship between electrical noise and inspection parameters, and proposes recommendations to reduce it. A time-varying filter empirical mode decomposition (TVF-EMD) joint wavelet thresholding method is proposed for denoising electrical noise after time-corrected gain (TCG). The ultrasonic echo signal goes through TVF-EMD decomposition, resulting in intrinsic mode functions (IMFs). Energy entropy is utilized to identify the signal-dominant IMFs, and wavelet thresholding is applied to further reduce noise within these selected IMFs. The proposed method effectively removes noise and ensures signal integrity by validating simulated and experimental signals.

This paper presents a novel ultrasonic signal denoising method that integrates adaptive variational mode decomposition (AVMD) with convolutional neural networks (CNNs). Initially, the whale optimisation algorithm (WOA) is employed to optimise key parameters of variational mode decomposition, specifically the decomposition modes K and the penalty factor α. The ultrasonic signals are then decomposed into intrinsic mode functions (IMFs) and various statistical feature parameters, such as energy entropy, sample entropy, kurtosis and correlation factors, are calculated for each IMF. The signal-to-noise ratio (SNR) of the reconstructed signal from the IMFs is used to assign label values, forming a feature dataset. Subsequently, a CNN is utilised to train and recognise this dataset, achieving an accuracy rate of 93.94% on the test set. The results demonstrate that the CNN effectively distinguishes between various IMF combinations based on the reconstructed SNR and can proficiently identify IMF combinations with higher SNR. Finally, denoising experiments on actual ultrasonic echo signals validate the feasibility of this method for noise reduction applications.

Aircraft assembly is an essential stage in the aircraft manufacturing industry, and the increasing complexity of aircraft functionality has put higher requirements for assembly quality. Various deep learning methods based on image or structural data have been used to predict assembly quality. However, these methods focus more on the structural relationships and interactions between assembled products and are difficult to adapt to scenarios where products and equipment may change. This paper proposes a graph convolutional neural network based on ontology modeling and spatial attention mechanism (Onto-SAGCN) for assembly quality analysis and prediction. Specifically, formal representations of assembly equipment, products, and assembly processes are established using ontologies, enabling the unified modeling of spatial and temporal relationships between entities and resolving the issues of data representation inconsistency. Then, the ontologies are transformed into undirected graphs, where nodes represent entities, node attributes represent feature data, and edges represent relationships or constraints between nodes. Designed to process data from the assembly process, the Onto-SAGCN model is subsequently applied to the assembly of aircraft moving wings. It predicts the diameters of holes by gathering operational data from equipment during the assembly and benchmarks these predictions against other established methods. Experimental outcomes affirm the method's elevated accuracy and dependability in forecasting assembly quality.

Scanning measurement systems are a pivotal solution to enable the rapid collection of 3D data from physical entities and provide further information for quality control. However, some external factors besides the scanner itself can also influence the measurement accuracy of the scanning system. In this paper, we propose a framework for analyzing the relative attitude influence of scanners on measurement uncertainty in the X and Z directions, respectively, containing the measured uncertainty calculation by the proposed optimal edge points detection and plane segmentation algorithm and the measurement uncertainty prediction model by machine learning. Experiments demonstrate that the distribution feature of measurement uncertainty differs in the X and Z directions and can provide predictions of the measurement uncertainty with about 0.2 % averaged MRE value. Our analysis advances the understanding of measurement uncertainty of scanning systems in different directions while it can be further applied to refine the calibration and measurement process.

In ultrasonic testing of Carbon Fiber Reinforced Polymers (CFRP), signals of near-surface flaws are often submerged in interface signals, resulting in blind spots for defect detection. To address this issue, this paper presents an autocorrelation imaging algorithm that combines Multipoint Optimal Minimum Entropy Deconvolution Adjusted (MOMEDA) with ${l_0}$l0-norm sparse representation. Firstly, the ultrasonic signal can be modelled as a convolution process. By using MOMEDA, the sequence of reflected pulses in the ultrasonic signal is obtained. Then, the ${l_0}$l0-norm sparse representation is utilised to enhance the temporal resolution of the sequence, successfully separating near-surface defect signals from interface signals. Finally, the processed data is input into the autocorrelation algorithm, achieving automated imaging of near-surface flaws. Simulation results demonstrate the efficacy of the algorithm in separating overlapping signals. Finally, experimental validation was conducted on flaws at three different depths. The algorithm presented in this paper is capable of identifying all near-surface flaws, with a defect size error rate consistently below 5%.

In recent years, the increasing complexity of quality control in aircraft assembly has necessitated the development of innovative and integrated solutions. This paper introduces a novel framework based on cloud-edge-end architecture to enhance quality monitoring systems in aircraft assembly production. The framework consists of five parts: cloud, edge, end, network, and the trained model. By leveraging the advantages of cloud computing, edge computing, and terminal devices, this framework aims to provide a comprehensive, real-time, and efficient quality monitoring solution. Key technologies within the cloud-edge-end framework are proposed, including data collection, storage, and transmission, deep learning methods based on Channel Attention Convolutional Neural Network (CACNN), and feedback mechanisms from the cloud layer to the lower layers. These innovations enable effective information sharing and utilization at various stages of the aircraft assembly process, providing operational managers with timely and accurate insights into assembly quality. A prototype system application has been implemented at an aircraft assembly site, facilitating process data monitoring and assembly status analysis. Experimental validation of the proposed CACNN algorithm has demonstrated its capability to monitor hole diameters online. Various case studies confirm the framework's effectiveness, showcasing substantial improvements in monitoring accuracy and operational efficiency.

During ultrasonic non-destructive testing of Carbon Fiber Reinforced Polymer (CFRP) composites, the signals from small near-surface defects are often overwhelmed by surface or bottom echoes, resulting in blind spots in defect detection. To address this issue, this paper proposes a novel self-correlation imaging algorithm that combines the Multi-Objective Minimum Entropy Deconvolution Algorithm (MOMEDA) with norm sparse representation. Firstly, the ultrasonic echo signals are modeled as a convolution process, and MOMEDA is employed to obtain the reflection pulse sequence of the ultrasonic signals. Next, the norm sparse representation is applied to improve the time resolution of the sequence, successfully separating the near-surface defect signals from the interface waves. Finally, the processed data is input into the self-correlation algorithm to achieve automated imaging of near-surface defects. Simulation results demonstrate the effectiveness of the proposed algorithm in separating overlapped echoes. Furthermore, experimental validation is conducted on three defects with different depths.

Temperature-variation-induced error compensation plays a pivotal role in large-scale metrology, in which the nonuniform temperature field would greatly influence the coordinate transformation process between different systems. This paper proposes a novel analytical thermal deformation compensation method with spatial discretisation modelling among 2D distributed enhanced reference system points to reduce the thermal influence on the coordinate transformation process. First, the analytical thermal deformation model based on the spatial discretisation is established with the plane thermal elastic mechanics and the Finite Difference Method. Then the nonuniform temperature field model is developed based on Ordinary Kriging spatial interpolation. Finally, a compensation method for the coordinate transformation process is proposed based on the actual measurement data and the proposed thermal deformation model. The experimental results illustrate the existence and significance of the nonuniform feature of the temperature field. By the proposed compensation method, the coordinate transformation error is reduced by 47.13% and 76.67% in the x and y directions, respectively. The method is applicable to diminish the influence of thermal deformation under a nonuniform temperature field on the coordinate transformation of large-scale metrology.

The application proportion of composite materials in the structure of modern aerospace vehicles has become an important indicator to measure progress,and the continuous improvement of the application level of composite materials is an important access to realize the lightweight,functionalization and intelligence of aerospace structures.However,the advanced application of composite materials is a complex system engineering,involving multiple fields such as materials,mechanics,design,manufacturing,control and computer science,and requiring the coordinated development of production-learning-research-application.Starting from the application cases in aviation structure manufacturing,the development direction of automated placement technologies for thermosetting composites are discussed by analyzing the technical demands for two typical aviation structures,large-size hyperbolic panel and specially-shaped rotary inlet.Furthermore,the current research and application status at home and abroad are comprehensively analyzed and summarized from three aspects:process planning technology,automated placement equipment development,and placement process planning software development,and the core technical issues faced in the manufacturing of advanced composite components are discussed.Then,developing the integrated capability of composite design and manufacturing is identified to require in-depth analysis of the complex coupling relationship among design,process and manufacturing factors,while process optimization and manufacturing feedback are achieved through single-factor analysis and multi-factor coordination mechanisms.Finally,the development ideas in the field of automated placement technology and equipment are proposed,aiming to provide directional reference for accelerating the construction of high-performance composite manufacturing capability for China.

Digital twins are proposed to manage the industrial production sites by mapping physical space entities to virtual spaces. Existing digital twin modeling methods are mainly realized through deploying several modules, including a physical module, a virtual module, a service module, a data module, and a connection module. However, the assembly environment of the aircraft assembly process is complex and challenging to manage. These methods do not provide the data flow and the intrinsic interaction between modules. As a consequence, a digital thread-based modeling digital twin (DTDT) framework with five modules is established in this paper, which takes advantage of both the digital twin and digital thread. Benefited by better data management, the DTDT framework would be conducive to the controllability and traceability of the manufacturing process and product quality. The key characteristics of products, process parameters, and equipment operation conditions, are stored in the database module. And then, the digital thread module will link the entity module and virtual module together by building the directed graph, in which interfaces are provided to the system services module for the process monitoring and processing quality prediction. Finally, the implementation of the DTDT framework is shown through a case of the drilling and riveting process in aircraft assembly. The comparative analysis confirms that this model can improve the efficiency of the aircraft assembly process.

In the digital drilling and riveting process of complex surfaces such as aircraft panels, the reference hole is pre-drilled on the skin surface. Generally, four laser displacement sensors (LDSs) are used as a group for normal adjustment. The camera vision is used to determine the position of the reference hole to obtain the accurate positioning of the drilling position. However, while dealing with large curvature complex panel surface and panel edge, four LDSs have the problem of reflection laser disappearance or matching failure. Applying two LDSs for normal adjustment on one side, the projection of the reference hole on the camera focal plane is an ellipse which means a further normal adjustment is desired in the direction of the ellipse’s minor axis. Therefore, this paper proposes a 3-dimensional pose estimation method (TDPEM) combining multi-sensor fusion and space geometry to realize the normal adjustment and position measurement of reference holes with a monocular camera and two LDSs. Firstly, two LDSs are used to adjust the reference hole’s horizontal (or vertical) direction. And then, an ellipse contour extraction algorithm is proposed to determine the ellipse parameters. Finally, the pose of the reference hole on the panel is determined by a spatial circle reverse algorithm. The experiment proves that the position error and angle error between this algorithm and the traditional four-LDS–based measurement method are within 0.03 mm and 0.2°, respectively, which verifies the feasibility and reliability of this algorithm.

For digital measurement demands of the structure and equipment installation in the internal and external regional space,the associate technology of inside and outside reference points is researched.The intemal measurement accuracy field united with the external measurement accuracy field is constructed.With regard to different openings between inside and outside spaces,utilizing the multi-spotting method,two measurement points and a level single-station positioning method,two measuring points and a level two-station positioning method,internal precision field is established to achieve the unity with external precision field.In a certain type of equipment,for example,experiment results show that the inconsistent measurement error of the same checkpoint between inside and outside measurement accuracy fields is up to 0.14 mm,which proves that the build mode is feasible,simple and reliable.

The invention discloses a posture positioning system for a leading edge assembly of an outer-ring wing box and belongs to the technical field of digital assembly of airplanes. The posture positioning system comprises an outer stand column, an inner stand column, a cross beam supported on the outer stand column and the inner stand column and a posture positioning device located below the cross beam and used for conducting posture positioning on the front edge assembly. The posture positioning device comprises an installation seat fixedly arranged on the cross beam, a telescopic seat suspended below the installation seat through a first spanwise guide rail sliding block mechanism, and a positioning mechanism fixedly connected with the telescopic seat. A telescopic part in the telescopic seat is of an aluminum alloy structure, and the end portion, close to the wing root of the leading edge assembly, of the telescopic seat is fixedly connected with the installation seat. By the adoption of the positioning system, the leading edge assembly and the posture positioning assembly can have thermal expansion compatibility in the spanwise direction, and the installation quality of the leading edge assembly is effectively improved while the installation efficiency of the leading edge assembly is improved.

根据增强参考系统点(ERS点)热变形的线性特点,建立ERS点线性热变形模型,利用线性热变形补偿系数矩阵对ERS点热变形进行补偿.提出一种基于Levenberg-Marquardt (LM)算法的线性热变形补偿系数矩阵优化方法,构建多组补偿后的ERS理论值与测量值之间最小加权距离误差函数的优化模型,采用LM算法求解.以壁板工装为例,通过仿真计算得出在多优化参数、多数据量情况下,优化时间在可以接受的工程应用范围内.通过实验验证了该方法有效地减小了转站误差,修正转站参数,得到的单位温度下的热变形系数矩阵稳定可靠.

为提高飞机附件安装的自动化和数字化水平,基于刚体运动学及广义坐标理论,构建了一种面向某型飞机附件校准系统的软件框架。该框架采用C/S多层架构体系,从装配关系表达、工艺流程管理、数据组织、计算框架和统计分析方法等方面,阐述了该系统的主要功能、子系统组成和若干关键技术。详细介绍了工艺管理系统和测量系统的工作模式及实现方法,包括数据存储和交换方法、激光跟踪仪的访问与控制技术等。应用实例表明,该系统能满足附件安装过程中各应用层次的自动化及数据管理需求,并为进一步优化装配流程、提高装配精度和效率提供数据基础。

在飞机数字化装配测量中,激光跟踪仪的转站精度决定着测量精度和装配质量,提高转站精度至关重要.而布置在工装上的公共观测点随温度变化发生的热变形,导致观测点偏离理论位置,往往是降低转站精度的主要原因.以壁板工装为实例,通过有限元模型仿真计算,得到观测点呈线性变形规律,并提出了用单位温度热变形系数矩阵来对理论坐标进行补偿的方法.根据仿真获得的变形规律,又提出了对大量实验数据统计分析来获得系数矩阵的方法.并采用回归分析的方法,检验了仿真和实验两种方式所获得的系数矩阵的相关性和等价性,表明仿真获得的系数矩阵的正确性.最后,用实例验证了工装上观测点热变形的线性关系和用热变形系数矩阵进行补偿的有效性.

In aircraft assembly, multiple laser trackers are used simultaneously to measure large-scale aircraft components. To combine the independent measurements, the transformation matrices between the laser trackers’ coordinate systems and the assembly coordinate system are calculated, by measuring the enhanced referring system (ERS) points. This article aims to understand the influence of the configuration of the ERS points that affect the transformation matrix errors, and then optimize the deployment of the ERS points to reduce the transformation matrix errors. To optimize the deployment of the ERS points, an explicit model is derived to estimate the transformation matrix errors. The estimation model is verified by the experiment implemented in the factory floor. Based on the proposed model, a group of sensitivity coefficients are derived to evaluate the quality of the configuration of the ERS points, and then several typical configurations of the ERS points are analyzed in detail with the sensitivity coefficients. Finally general guidance is established to instruct the deployment of the ERS points in the aspects of the layout, the volume size and the number of the ERS points, as well as the position and orientation of the assembly coordinate system.

The basic principle of coordinate system registration method was introduced in order to evaluate the measurement accuracy of large‐scale metrology system in aircraft assembly .Then an explicit model was derived to mathematically describe the transformation parameter errors and the registration error .The model reveals the impacts of the layout of the enhanced reference system ( ERS ) points and the measurement errors of the ERS points of the laser tracker . T he uncertainty evaluation of the transformation parameter errors and the estimation of the registration error were fulfilled based on the derived model . Monte‐Carlo simulation was performed to simulate the coordinate system registration process .The proposed uncertainty evaluation method and the estimation method were validated .