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This paper aims to promote the integration of the relative position accuracy (RPA) measurement and evaluation in digital assembly process by adopting the model-based method. An integrated framework for RPA measurement is proposed based on a model-based definition (MBD) data set. The study also aims to promote the efficiency of inspection planning of RPA measurement by improving the reusability and configurability of the inspection planning.

The works have been carried out on three layers. In the data layer, an extended MBD data set is constructed to describe the objects and data for defining RPA measurement items; In definition layer, a model based and hierarchical structure for RPA item definition is constructed to support quick definition for RPA measurement items. In function layer, a toolset consisting three modules is constructed in a sequence from measurement planning to RPA value solving to visualized displaying again. Based on this framework, a prototype system is developed.

The paper provides an identified practice of model-based inspection. It suggests that MBD is valuable in promoting both the integration and efficiency of digital inspection.

The templates and constructed geometry objects given in this paper are still limited in a scenario of aircraft assembly. The integrity and universality of them still need follow-up works.

The paper includes implications for the model based digital inspection, the digital assembly and the extended application of MBD.

This paper expands the application of MBD in inspection and fulfils the need to promote the integration and efficiency of digital inspection in large-scale component assembly.

Although data rich building information models have been widely adopted in the Architecture Engineering and Construction (AEC) industry in the United Kingdom (UK), use of 2D drawings on site for construction is still the norm. The ability of 2-dimensional (2D) drawings to convey complex 3-dimensional information is limited and requires interpretation from operatives, and 2D drawings can be quickly superseded by model updates. Although constructing directly from a model has been adopted in the aerospace and automotive industries, its use in construction is in its infancy. This research therefore aims to investigate the potential for, and barriers to, model-based construction in the UK.

This research uses a qualitative approach, thematically analysing 13 semi-structured interviews with UK-based construction professionals who have experience of paperless or model-based construction.

Although model -based construction has been implemented to a limited extent on some civil engineering projects; research and investment in software, network capacity, legal and contractual issues, and cultural and human factors will need to be considered before model-based construction can be implemented more widely.

The research contributes to an understudied, emergent area of construction practice and outlines hurdles that need to be understood and overcome before more widespread adoption of model-based construction can take place.

The purpose of this paper is to study the modelling mechanisms of several grey incidence analysis models with great influence, including Deng’s grey incidence model, absolute degree of grey incidence model, slope degree of incidence model, similitude degree of incidence model and closeness degree of incidence model; then analyse the problems to be solved in grey incidence analysis models; and clarify the applicable ranges of commonly used grey incidence models.

The paper comes to conclusions by means of comparable analysis. The authors compare several commonly used grey incidence analysis models, including Deng’s grey incidence model, absolute degree of grey incidence model, slope degree of incidence model, similitude degree of incidence model and closeness degree of incidence model and give several examples to clarify the reasons why quantitative analysis results of different models are not exactly the same.

As the intension of each kind of incidence model is clear and the extension is relatively obscure, grey incidence orders calculated by different incidence models are often different. When making actual decisions, incompatible results may appear. According to different characteristics of extraction, grey incidence analysis models can be divided into three types: incidence model based on closeness perspective, incidence model based on similarity perspective and incidence model based on comprehensive perspective.

The conclusions obtained in this paper can help people avoid some defects in the process of actual selection and choose the better incidence analysis model.

The conclusions can be used as a reference and basis for the selection of grey incidence analysis models, it can help to overcome the defects and shortcomings of models caused by themselves and screen out more excellent analytical models.

Three-dimensional digital design and manufacturing technology are changing the current manufacturing pattern and have become the core of enterprise competition. However, the research and application of three-dimensional digital technology in the present phase have a strong bias toward the design of three-dimensional model and focus little on process planning. It restricts the development of manufacturing industry. Therefore, this paper aims to present a design scheme of three-dimensional digital process planning.

A three-dimensional digital process design method is developed by combining model-based definition technology and knowledge engineering technology. Model-based definition technology is used to display the process information. And knowledge engineering technology is used for process decision; meanwhile, ontology technology is introduced to describe process knowledge. And taking shaft part as an example, this paper establishes the general ontology of manufacturing process and the special ontology of shaft. This research provides an available method for the three-dimensional digital process planning.

Traditional process planning mainly is based on two-dimensional engineering drawing, which leads to the low efficiency and quality of process planning. Moreover, it cannot achieve effective mining and management of knowledge. Thus, applying an effective knowledge management technology into a three-dimensional process system is necessary.

This paper contributes to an available method for three-dimensional digital process planning.

The introduction of model-based definition technology makes process information display in three-dimensional environment. And ontology technology achieves sematic reference and efficient management of process knowledge.

The purpose of this paper is to construct an interval grey number NGM(1,1) direct prediction model (abbreviated as IGNGM(1,1)), which need not transform interval grey numbers sequences into real number sequences, and the Markov model is used to optimize residual sequences of IGNGM(1,1) model.

A definition equation of IGNGM(1,1) model is proposed in this paper, and its time response function is solved by recursive iteration method. Next, the optimal weight of development coefficients of two boundaries is obtained by genetic algorithm, which is designed by minimizing the average relative error based on time weighted. In addition to that, the Markov model is used to modify residual sequences.

The interval grey numbers’ sequences can be predicted directly by IGNGM(1,1) model and its residual sequences can be amended by Markov model. A case study shows that the proposed model has higher accuracy in prediction.

Uncertainty and volatility information is widespread in practical applications, and the information can be characterized by interval grey numbers. In this paper, an interval grey numbers direct prediction model is proposed, which provides a method for predicting the uncertainty information in the real world.

The main contribution of this paper is to propose an IGNGM(1,1) model which can realize interval grey numbers prediction without transforming them into real number and solve the optimal weight of integral development coefficient by genetic algorithm so as to avoid the distortion of prediction results. Moreover, the Markov model is used to modify residual sequences to further improve the modeling accuracy.

The possibility function-based grey clustering model has evolved into a complete approach for dealing with uncertainty evaluation problems. Existing models still have problems with the choice dilemma of the maximum criteria and instances when the possibility function may not accurately capture the data's randomness. This study aims to propose a multi-stage skewed grey cloud clustering model that blends grey and randomness to overcome these problems.

First, the skewed grey cloud possibility (SGCP) function is defined, and its digital characteristics demonstrate that a normal cloud is a particular instance of a skewed cloud. Second, the border of the decision paradox of the maximum criterion is established. Third, using the skewed grey cloud kernel weight (SGCKW) transformation as a tool, the multi-stage skewed grey cloud clustering coefficient (SGCCC) vector is calculated and research items are clustered according to this multi-stage SGCCC vector with overall features. Finally, the multi-stage skewed grey cloud clustering model's solution steps are then provided.

The results of applying the model to the assessment of college students' capacity for innovation and entrepreneurship revealed that, in comparison to the traditional grey clustering model and the two-stage grey cloud clustering evaluation model, the proposed model's clustering results have higher identification and stability, which partially resolves the decision paradox of the maximum criterion.

Compared with current models, the proposed model in this study can dynamically depict the clustering process through multi-stage clustering, ensuring the stability and integrity of the clustering results and advancing grey system theory.

The purpose of this paper is to propose a modification of the Functional Requirements for Authority Data (FRAD) model to accommodate names in non-Roman languages with diverse writing systems.

The model modifies and clarifies the attributes and relationships regarding non-Roman languages of the current FRAD model, based on the definitions of transliteration, transcription, and Romanization in ISO 5127:2001. The modified model overcomes the problems inherent in the current FRAD model when handling writing systems in non-Roman languages.

The proposed model clearly shows the hierarchy of Controlled Access Points (CAPs). CAPs are either based directly on names or derived from other CAPs. For name or person identification, a CAP and its derivations should be treated as a set. The proposed model also resolves ambiguity in the definition of the word transliteration in the current FRAD model.

By establishing new relationships and amending the attributes of the FRAD model, the proposed model better expresses personal names in non-Roman languages as well as names in western languages. It also organizes complex writing systems of non-Roman languages. The proposed model could lead to an improved format for authority data and will facilitate international sharing of data.

Managing components' resources plays a critical role in the success of systems' architectures designed for cyber–physical systems (CPS). Performing the selection of candidate components to pursue a specific application's needs also involves identifying the relationships among architectural components, the network and the physical process, as the system characteristics and properties are related.

Using a Model-Driven Engineering (MDE) approach is a valuable asset therefore. Within this context, the authors present the so-called Systems Architecture Ontology (SAO), which allows the representation of a system architecture (SA), as well as the relationships, characteristics and properties of a CPS application.

SAO uses a common vocabulary inspired by the Architecture Analysis and Design Language (AADL) standard. To demonstrate SAO's applicability, this paper presents its use as an MDE approach combined with ontology-based modeling through the Ontology Web Language (OWL). From OWL models based on SAO, the authors propose a model transformation tool to extract data related to architectural modeling in AADL code, allowing the creation of a components' library and a property set model. Besides saving design time by automatically generating many lines of code, such code is less error-prone, that is, without inconsistencies.

To illustrate the proposal, the authors present a case study in the aerospace domain with the application of SAO and its transformation tool. As result, a library containing 74 components and a related set of properties are automatically generated to support architectural design and evaluation.

Effective management and utilisation of plant history data can considerably improve plant and equipment performance. This rationale underpins statistical and mathematical models for exploiting plant management data more efficiently, but industry has been slow to adopt these models. Reasons proffered for this include: a perception of models being too complex and time consuming; and an inability of their being able to account for dynamism inherent within data sets. To help address this situation, this research developed and tested a web‐based data capture and information management system. Specifically, the system represents integration of a web‐enabled relational database management system (RDBMS) with a model base management system (MBMS). The RDBMS captures historical data from geographically dispersed plant sites, while the MBMS hosts a set of (Autoregressive Integrated Moving Average – ARIMA) time series models to predict plant breakdown. Using a sample of plant history file data, the system and ARIMA predictive capacity were tested. As a measure of model error, the Mean Absolute Deviation (MAD) ranged between 5.34 and 11.07 per cent for the plant items used in the test. The Root Mean Square Error (RMSE) values also showed similar trends, with the prediction model yielding the highest value of 29.79 per cent. The paper concludes with direction for future work, which includes refining the Graphical User Interface (GUI) and developing a Knowledge Based Management System (KBMS) to interface with the RDBMS.