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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 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.

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.

Material variation is inevitable in volume production, especially the sheet metal thickness variation, which influences part stiffness characteristic. The purpose of this paper is to present a new variation model of compliant sheet metal assembly with consideration of material variation influence.

The theory of computational solid mechanics is used to obtain the relationship between part stiffness matrix and material characteristic. The method of influence coefficients is adopted to deduce the assembly variation model.

Material variation‐induced influence coefficients to assembly variation are obtained, and a variation model of compliant sheet metal assembly with sources of material variations, part geometric variations and fixture variations is presented. Analysis shows that material variation has an important influence to assembly variations.

A quantitative relationship between assembly variations and material thickness variations is firstly given and a new variation model of compliant sheet metal assembly is presented to help designers to more exactly predict the assembly variation and diagnose variation sources.

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.

Process variation has become a major concern in the design of many nanometer circuits, including interconnect pipelines. The purpose of this paper is to provide a comprehensive overview of types and sources of all aspects of interconnect process variations.

The impacts of these interconnect process variations on circuit delay and cross‐talk noises along with the two major sources of delays – parametric delay variations and global interconnect delays – have been discussed.

Parametric delay evaluation under process variation method avoids multiple parasitic extractions and multiple delay evaluations as is done in the traditional response surface method. This results in significant speedup. Furthermore, both systematic and random process variations have been contemplated. The systematic variations need to be experimentally modeled and calibrated while the random variations are inherent fluctuations in process parameters due to any reason in manufacturing and hence are non‐deterministic.

This paper usefully reviews process variation effects on very large‐scale integration (VLSI) interconnect.

This research seeks to present a methodology for investigating the generalizability of a theory‐testing model. The methodology is used to examine the generalizability of a model of the antecedents and consequences of customer delight.

Theory testing of models in the marketing often fails to define an intended universe of generalization. This paper shows how multivariate generalizability theory can be used to estimate construct covariance components for specific sources of variance. These components can then be used to assess the generalizability of a structural equation model of a marketing phenomenon.

The parameters of a model of customer delight obtained from data that sample customers of a service or data that confound sources of variance do not generalize to data that capture variation across services or variation across raters. The relative impact of customer delight and satisfaction on behavioral intention varies with the source of variation being studied.

Previous research suggests that after controlling for customer satisfaction, customer delight accounts for very little variation in behavioral intention. But, for the source of variation of most relevance to managers, namely web sites, it is customer delight, not customer satisfaction, that is strongly associated with behavioral intention.

The methodology can be applied and can produce model parameters having substantially different managerial implications for the management of customer satisfaction and customer delight.

The purpose of this paper is to focus on optimal sensor placement for the fixture variation diagnosis of compliant sheet metal assembly process. Fixture variations are the main sources for complex automotive body dimensional failures. An effective measurement strategy can help exactly and timely diagnose these fixture variations. Research on sensor placement strategy of compliant sheet metal assembly process is not much stated formerly.

The impact principle of fixture variations is analyzed to set up the relationship between the assembly variation and fixture variations applying the method of influence coefficients and the effective independence (EI) method is used to find the optimal sensor positions based on the impact principle analysis of fixture variations.

The obtained fixture variation sensitivity matrix describes the influence of fixture variations to compliant sheet metal assembly variation and can be used for diagnosing fixture variations. The EI method can effectively solve the optimal sensor positions for compliant sheet metal assembly by a case demonstration.

The proposed method can solve the sensor placement of online assembly station for diagnosing fixture variations. It takes the compliant characteristics of sheet metal parts into account and the sensor information has much greater diagnosability than that from applying other methods.

This research is elucidating quality control theories to reduce variation in chocolate manufacturing process in the UK food company that will help maintain the processes stable and predictable. The purpose of this paper is to reduce defects of the output; to identify the root causes of variation; to establish and implement solutions to this variation problem; and to establish a control system to monitor and report any variation in the process.

The authors use experimental case study of a chocolate company to achieve the objective. In this paper, the authors predominantly use established theory define–measure–analyse–improve–control, customised to the case of the chocolate factory to reduce variations in production processes.

The results confirm that customised-traditional theoretical quality models will support manufacturing companies to maintain customer satisfaction while enhancing quality and reliability.

Implementation of customised approach reduced the rate of defect from 8 to 3.7 per cent. The implications of reduced variation are improved product quality; reprocessing elimination; and a more stable process that support sustainability and reliability in producing chocolates to meet customer needs.

The authors used an experimental-based case study approach to test with one company. Testing in multiple case companies may help to generalise results.

The research study experimentally tested quality approach with a real case company and hence the findings of this study can be applied to other cases working in similar settings.

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.