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Modeling and analysis of dimensional variation propagation is a crucial support technology for variation reduction, product/process design evaluation and recognition of variation source. However, owing to the multi-deviation (i.e. part deviations and fixture deviations) and multi-interaction (i.e. part-to-part interaction, part-to-fixture interaction and station-to-station interaction) in assembly processes, it is difficult for designers to describe or understand the variation propagation (or accumulation) mechanism clearly. The purpose of this paper is to propose a variation propagation modeling and analysis (VPMA) method based on multiple constraints aiming at a single station.

Initially, part-to-part constraints (PPCs) and part-to-fixture constraints (PFCs) are applied for the multi-interaction of assembly, and multiple constraints graph (MCG) model is proposed for expressing PPCs, PFCs, parts, as well as the variation propagation relation among them. Then, locating points (LPs) are adopted for representing the deviations in constraints, and formulas for calculating the deviations of LPs are derived. On that basis, a linearized relation between LPs’ deviations and part’s locating deviations is derived. Finally, a wing box is presented to validate the proposed method, and the results indicate the methodology’s feasibility.

MCG is an effective tool for dimensional VPMA, which is shown as an example of this paper.

Functions of geometric constraints in dimensional variation propagation are revealed, and MCG is proposed to formulize dimensional variation propagation.

To enhance an entrepreneur's business network through the integration of the social network concepts and design principles of virtual communities.

This study documents the design and initial deployment of a virtual community case, Innovation Information Infrastructure, based on social network concepts.

Basic design principles, deployment strategy, and future directions for social network‐based virtual communities are presented.

Because of resource and time constraints, only basic content service, communication tools, and transaction functions were implemented in the initial deployment. Future extensions of this study may include development of a personalized and intelligent information retrieval system utilizing data mining techniques, development of advanced communication features to promote active participation, and creation of automatic social network‐tracking tools to monitor an individual's network evolution.

Sample designs are provided for researchers who are interested in conducting a similar study.

This study serves as a starting‐point for how theories from social network and virtual community research streams can be applied in creating a virtual community that facilitates transformation of knowledge into products and services, which in turn lead to greater economic prosperity.

The purpose of this paper is to propose a unified optimization design method and apply it to handle the brake squeal instability involving various uncertainties in a unified framework.

Fuzzy random variables are taken as equivalent variables of conventional uncertain variables, and a unified response analysis method is first derived based on level-cut technique, Taylor expansion and central difference scheme. Next, a unified reliability analysis method is developed by integrating the unified response analysis and fuzzy possibility theory. Finally, based on the unified reliability analysis method, a unified reliability-based optimization model is established, which is capable of optimizing uncertain responses in a unified way for different uncertainty cases.

The proposed method is extended to perform squeal instability analysis and optimization involving various uncertainties. Numerical examples under eight uncertainty cases are provided and the results demonstrate the effectiveness of the proposed method.

Most of the existing methods of uncertainty analysis and optimization are merely effective in tackling one uncertainty case. The proposed method is able to handle the uncertain problems involving various types of uncertainties in a unified way.

This paper aims to develop an efficient numerical method for mid-frequency analysis of built-up structures with large convex uncertainties.

Based on the Chebyshev polynomial approximation technique, a Chebyshev convex method (CCM) combined with the hybrid finite element/statistical energy analysis (FE-SEA) framework is proposed to fulfil the purpose. In CCM, the Chebyshev polynomials for approximating the response functions of built-up structures are constructed over the uncertain domain by using the marginal intervals of convex parameters; the bounds of the response functions are calculated by applying the convex Monte–Carlo simulation to the approximate functions. A relative improvement method is introduced to evaluate the truncated order of CCM.

CCM has an advantage in accuracy over CPM when the considered order is the same. Furthermore, it is readily to consider the CCM with the higher order terms of the Chebyshev polynomials for handling the larger convex parametric uncertainty, and the truncated order can be effectively evaluated by the relative improvement method.

The proposed CCM combined with FE-SEA is the first endeavor to efficiently handling large convex uncertainty in mid-frequency vibro-acoustic analysis of built-up structures. It also has the potential to serve as a powerful tool for other kinds of system analysis when large convex uncertainty is involved.

The purpose of this paper is to develop an effective treatment to analyze and diagnose urban rail transit (URT) vehicle maintenance strategy.

In this paper, the technique of Failure Mode and Effects Analysis (FMEA) is introduced into the examination of URT trains, first. Then the method of fuzzy-set-based assessment for FMEA is presented, which is the quantitative tool of Fuzzy-Set-based treatment for FMEA in analysis and diagnoses to URT maintenance strategy. Moreover, recommendations for further improvement of the proposed approach are also provided. Initial application into the vehicle maintenance of Shanghai URT System shows, that the proposed approach has a good performance and consequently is worth further development.

The paper presents a FMEA and fuzzy-set-based theoretical approach for analyzing and diagnosing current methods in URT vehicle maintenance strategy.

With rapid development of URT systems in the world especially in those highly populated areas, much more attentions are turning to researches on URT maintenance, nevertheless, few quantitative research achievement are mentioned or applied. This paper is a tentative attempt at introducing fuzzy-set theory into quantitative analysis and diagnoses of URT maintenance strategy.

The study in this paper is helpful in theory and practice of URT maintenance and its methodology could be further applied into a broad family of facility group or system in other engineering fields.

The purpose of this paper is to facilitate autonomous landing of a multi-rotor unmanned aerial vehicle (UAV) on a moving/tilting platform using a robust vision-based approach.

Autonomous landing of a multi-rotor UAV on a moving or tilting platform of unknown orientation in a GPS-denied and vision-compromised environment presents a challenge to common autopilot systems. The paper proposes a robust visual data processing system based on targets’ Oriented FAST and Rotated BRIEF features to estimate the UAV’s three-dimensional pose in real time.

The system is able to visually locate and identify the unique landing platform based on a cooperative marker with an error rate of 1° or less for all roll, pitch and yaw angles.

The proposed vision-based system aims at on-board use and increased reliability without a significant change to the computational load of the UAV.

The simplicity of the training procedure gives the process the flexibility needed to use a marker of any unknown/irregular shape or dimension. The process can be easily tweaked to respond to different cooperative markers. The on-board computationally inexpensive process can be added to off-the-shelf autopilots.

The purpose of this study is to present a new and relatively inexpensive method for posture evaluation of the positioning of the wing-body assembly. Positioning is an essential process to guarantee alignment accuracy in an assembly line.

The studied method includes a structural set-up and a software algorithm used to process a set of experimental input data to compute the actual position of the wing with respect to the ideal position, which is proposed considering measurement uncertainty, the deviation caused by large errors in measurement points and the different tolerance requirements.

The studied method has been found to be simple and effective in addition to being highly accurate. Compared with most of the current methods that have been developed with optical equipment, it is more cost- and space-efficient. The automation process determines how much operation time will be saved.

The studied method has been applied in an actual assembly line, and the economic and time savings illustrate its benefits.

This method provides an attractive wing-body assembly solution for those enterprises that want to find a low-cost option or have limited measuring space for optical equipment. It can also be the basis for the accurate assembly of other large parts for aircraft and other vessels.