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Driven by the development in sensing techniques and information and communications technology, and their applications in the manufacturing system, data-driven quality control methods play an essential role in the quality improvement of assembly products. This paper aims to review the development of data-driven modeling methods for process monitoring and fault diagnosis in multi-station assembly systems. Furthermore, the authors discuss the applications of the methods proposed and present suggestions for future studies in data mining for quality control in product assembly.

This paper provides an outline of data-driven process monitoring and fault diagnosis methods for reduction in variation. The development of statistical process monitoring techniques and diagnosis methods, such as pattern matching, estimation-based analysis and artificial intelligence-based diagnostics, is introduced.

A classification structure for data-driven process control techniques and the limitations of their applications in multi-station assembly processes are discussed. From the perspective of the engineering requirements of real, dynamic, nonlinear and uncertain assembly systems, future trends in sensing system location, data mining and data fusion techniques for variation reduction are suggested.

This paper reveals the development of process monitoring and fault diagnosis techniques, and their applications in variation reduction in multi-station assembly.

This paper aims to present a modeling and analysis approach for multi-station aircraft assembly to predict assembly variation. The variation accumulated in the assembly process will influence the dimensional accuracy and fatigue life of airframes. However, in digital large aircraft assembly, variation propagation analysis and modeling are still unresolved issues.

Based on an elastic structure model and variation model of multistage assembly in one station, the propagation of key characteristics, assembly reference and measurement errors are introduced. Moreover, the reposition and posture coordination are considered as major aspects. The reposition of assembly objects in a different assembly station is described using transformation and blocking of coefficient matrix in finite element equation. The posture coordination of the objects is described using homogeneous matrix multiplication. Then, the variation propagation model and analysis of large aircraft assembly are established using a discrete system diagram.

This modeling and analysis approach for multi-station aircraft assembly reveals the basic rule of variation propagation between adjacent assembly stations and can be used to predict assembly variation or potential dimension problems at a preliminary assembly phase.

The modeling and analysis approaches have been used in a transport aircraft project, and the calculated results were shown to be a good prediction of variation in the actual assembly.

Although certain simplifications and assumptions have been imposed, the proposed method provides a better understanding of the multi-station assembly process and creates an analytical foundation for further work on variation control and tolerance optimization.

Dimensional variation management is a major challenge in multi‐station sheet metal assembly processes involving complex products such as automotive body and aircraft fuselage assemblies. Very few studies have explored it at a preliminary design phase taking into consideration effects of part deformation on variation propagation, since early design phase involves the development of imprecise design models with scant or incomplete product and process knowledge. The objective of this paper is to present a variation model which can be built into the preliminary design phase taking into consideration all of the existing interactions between flexible parts and tools in multi‐station sheet metal assembly process.

The paper addresses this problem by first, presenting a beam‐based product and process model which shares the same data structure of the B‐Rep CAD models, and therefore can be embedded in CAD systems for automatic product skeletal design; second, determining the influence of part deformation, for various, differing joining and releasing schemes, on variation propagation; and third, utilizing this information to generate a vector‐based variation propagation model for multistation sheet metal assemblies.

This paper presents a beam‐based product and process model which shares the same data structure of the B‐Rep CAD models, and therefore can be embedded in CAD systems for automatic product skeletal design; determines the influence of part deformation, for various, differing joining and releasing schemes, on variation propagation; and utilizes this information to generate a vector‐based variation propagation model for multistation sheet metal assemblies.

A truck cab assembly is presented to demonstrate the advantages of the proposed model over the state‐of‐the‐art approach used in industry for sheet metal assemblies.

This paper aims to consider a problem of assembly sensitivity in a multi-station assembly process. The authors focus on the assembly process of aircrafts, which includes cabins and inertial navigation system (INSs), and establish the assembly process state space model for their assembly sensitivity research.

To date, the process-related errors that cause large variations in key product characteristics remains one of the most critical research topics in assembly sensitivity analysis. This paper focuses on the unique challenges brought about by the multi-station system: a system-level model for characterizing the variation propagation in the entire process, and the necessity of describing the system response to variation inputs at both station-level and single fixture-level scales. State space representation is used to describe the propagation of variation in such a multi-station process, incorporating assembly process parameters such as fixture-locating layout at individual stations and station-to-station locating layout change.

Following the sensitivity analysis in control theory, a group of hierarchical sensitivity indices is defined and expressed in terms of the system matrices in the state space model, which are determined by the given assembly process parameters.

A case study of assembly sensitivity for a multi-station assembly process illustrates and validates the proposed methodology.

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.

Coordination feature (CF) is the information carrier in dimension and shape transfer process in aircraft manufacturing. The change of its geometric size, shape, position or other attributes would affect the consistency of accumulated errors between two or more assemblies. To identify these “key characteristics” that have a close relationship with the assembly precision, a comprehensive method was developed under digital manufacturing environment, which was based on importance calculation. The multi-hierarchy and multi-station assembly process of aircraft products were also taken into consideration.

First, the interaction and evaluation relationship between components at different manufacturing stages was decomposed with a hierarchical net. Second, to meet coordination accuracy requirements, with the integrated application of Taguchi quality loss function, accuracy principal and error correction coefficient H, the quality loss between target features and candidate features at adjacent assembly hierarchies were calculated, which was based on their precision variation. Third, the influence degree and affected degree of the features were calculated with DEMATEL (decision-making trial and evaluation laboratory) method, and the concepts of centrality degree index and cause degree index were proposed for calculating the complete importance degree to eventually identify the CFs.

Based on the proposed methodology, CFs, affecting the skin profile and the flush coordination accuracy, were successfully identified at different assembly hierarchies to a certain type of wing flap component.

Benefit results for the engineering application showed that the deviation of skin profile was more accurate than before, and the tolerance was also closer to the centerline of required assembly precision range. Moreover, the stability in the assembly process was increased by 26.9 per cent, which could bring a higher assembly quality and an enhancement on aircraft’s flight performance.

This study aims to present a model and analysis of automotive body outer cover panels (OCPs) assembly systems to predict assembly variation. In the automotive industry, the OCPs assembly process directly influences the quality of the automobile body appearance. However, suitable models to describe variation propagation of OCPs assembly systems remain unknown.

An adaptive state space model for OCPs assembly systems is introduced to accurately express variation propagation, including variation accumulation and transition, where two compliant deviations make impacts on key product characteristics (KPCs) of OCP, and the impacts are accumulated from welding process to threaded connection process. Another new source of variation from threaded connection is included in this model. To quantify the influence of variation from threaded connection on variation propagation, the threaded connection sensitivity matrix is introduced to build up a linear relationship between deviation from threaded connection and output deviation in KPCs. This matrix is solved by homogeneous coordinate transformation. The final deviation of KPCs will be transferred to ensure gaps and flushes between two OCPs, and the transition matrix is considered as a unit matrix to build up the transition relationship between different states.

A practical case on the left side body structure is described, where simulation result of variation propagation reveals the basic rule of variation propagation and the significant effect of variation from threaded connection on variation propagation of OCPs assembly system.

The model can be used to predict assembly variation or potential dimension problems at a preliminary assembly phase. The calculated results of assembly variation guide designers or technicians on tolerance allocation, fixture layout design and process planning.

This paper aims to present a combination modeling method using multi-source information in the process to improve the accuracy of the dimension propagation relationship for assembly variation reduction.

Based on a variable weight combination prediction method, the combination model that takes the mechanism model and data-driven model based on inspection data into consideration is established. Furthermore, the combination model is applied to qualification rate prediction for process alarming based on the Monte Carlo simulation and also used in engineering tolerance confirmation in mass production stage.

The combination model of variable weights considers both the static theoretical mechanic variation propagation model and the dynamic variation relationships from the regression model based on data collections, and provides more accurate assembly deviation predictions for process alarming.

A combination modeling method could be used to provide more accurate variation predictions and new engineering tolerance design procedures for the assembly process.

The use of industrial robots in the advanced countries of the world is growing. Whilst generally the concept of a robot is taken as a highly versatile human‐like device the term also extends to include much simpler devices of the pick‐and‐place type with a fixed sequence of events and these form by far the largest proportion of the world's robot population. Whilst they lack versatility in themselves they often form part of a much more complex automatic system in which some degree of flexibility is required. In addition they must operate at their optimum rate whilst being fail‐safe in operation. The design of a suitable control system to meet such demands particularly when a number of such devices and the primary process machinery have to be interlinked can be solved with the aid of sequential switching theory.

The purpose of this study is to introduce the top-level design ideas and the overall architecture of earthquake early-warning system for high speed railways in China, which is based on P-wave earthquake early-warning and multiple ways of rapid treatment.

The paper describes the key technologies that are involved in the development of the system, such as P-wave identification and earthquake early-warning, multi-source seismic information fusion and earthquake emergency treatment technologies. The paper also presents the test results of the system, which show that it has complete functions and its major performance indicators meet the design requirements.

The study demonstrates that the high speed railways earthquake early-warning system serves as an important technical tool for high speed railways to cope with the threat of earthquake to the operation safety. The key technical indicators of the system have excellent performance: The first report time of the P-wave is less than three seconds. From the first arrival of P-wave to the beginning of train braking, the total delay of onboard emergency treatment is 3.63 seconds under 95% probability. The average total delay for power failures triggered by substations is 3.3 seconds.

The paper provides a valuable reference for the research and development of earthquake early-warning system for high speed railways in other countries and regions. It also contributes to the earthquake prevention and disaster reduction efforts.