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Complex mechanical 3D computer-aided design (CAD) model embodies rich implicit design knowledge. Through discovering the key function parts and key function module in 3D CAD assembly model in advance, it can promote the designers’ understanding and reuse efficiency of 3D assembly model in design reuse.

An approach for discovering key function module in complex mechanical 3D CAD assembly model is proposed. First, assembly network for 3D CAD assembly model is constructed, where the topology structure characteristics of 3D assembly model are analyzed based on complex network centrality. The degree centrality, closeness centrality, betweenness centrality and mutual information of node are used to evaluate the importance of the parts in 3D assembly model. Then, a multi-attribute decision model for part-node importance is established, and the comprehensive evaluation for key function parts in 3D assembly model is accomplished by combining Analytic Hierarchy Process and Technique for Order Preference by Similarity to an Ideal Solution (TOPSIS). Subsequently, a community discovery of function module in assembly model-based Clauset–Newman–Moore (CNM)-Centrality is given in details. Finally, 3D CAD assembly model of worm gear reducer is taken as an example to verify the effectiveness and feasibility of proposed method.

The key function part in CAD assembly model is evaluated comprehensively considering assembly topology more objectively. In addition, the key function module containing key function part is discovered from CAD assembly model by using CNM-Centrality-based community discovery.

The approach can be used for discovering important design knowledge from complex CAD assembly model when reusing the assembly model. It can help designers capture and understand the design thinking and intent, improve the reuse efficiency and quality.

The paper first proposes an approach for discovering key function module in complex mechanical 3D CAD assembly model taking advantage of complex network theory, where the key function part is evaluated using node centrality and TOPSIS, and the key function module is identified based on community discovery.

In this paper, an assembly sequence planning system, based on binary vector representations, is developed. The neural network approach has been employed for analyzing optimum assembly sequence for assembly systems.

The input to the assembly system is the assembly's connection graph that represents parts and relations between these parts. The output to the system is the optimum assembly sequence. In the constitution of assembly's connection graph, a different approach employing contact matrices and Boolean operators has been used. Moreover, the neural network approach is used in the determination of optimum assembly sequence. The inputs to the networks are the collection of assembly sequence data. This data is used to train the network using the back propagation (BP) algorithm.

The proposed neural network model outperforms the available assembly sequence‐planning model in predicting the optimum assembly sequence for mechanical parts. Due to the parallel structure and fast learning of neural network, this kind of algorithm will be utilized to model another types of assembly systems.

In the proposed neural approach, the back propagation algorithm is used. Various training algorithms can be employed.

The simulation results suggest that the neural predictor would be used as a predictor for possible practical applications on modeling assembly sequence planning system.

This paper discusses a new modelling scheme known as artificial neural networks. The neural network approach has been employed for analyzing feasible assembly sequences and optimum assembly sequence for assembly systems.

The purpose of this paper is to find a method for key assembly structure identification in complex mechanical assembly. Three-dimensional model reuse plays an increasingly important role in complex product design and innovative design. Assembly model has become important resource of models reuse in enterprises, which contains certain function assembly structures. These assembly structures implicating plenty of design intent and design experience knowledge can be used to support function-structure design, modular design reuse and semantics analysis for complex product.

A method for identifying key assembly structures in assembly model is presented from the viewpoint of assembly topology and multi-source attributes. First, assembly model is represented based on complex network. Then, a two-level evaluation model is put forward to evaluate importance of parts assembled, and the key function parts in assembly can be obtained. After that, on the basis of the function parts, a heuristic algorithm upon breadth first searching is given to identify key assembly structures.

The method could be used to evaluate key function parts and identify key assembly structures in complex mechanical assembly according to the specific circumstances.

The method can not only help designers find the key assembly structure in complex mechanical assembly model, facilitate the function-structure designing and semantics analyzing, and thereby improve the efficiency of product knowledge reuse, but also assist in analyzing influence scope of key function part changing and optimization of the assembly process for complex mechanical assembly.

The paper is the first to propose a method for key assembly structure identification in complex mechanical assembly, where the key function parts can be evaluated through a two-level evaluation model, and the key assembly structures are identified automatically based on complex network.

This paper aims to review seven artificial intelligence tools that are useful in assembly automation: knowledge‐based systems, fuzzy logic, automatic knowledge acquisition, neural networks, genetic algorithms, case‐based reasoning and ambient‐intelligence.

Each artificial intelligence tool is outlined, together with some examples of their use in assembly automation.

Artificial intelligence has produced a number of useful and powerful tools. This paper reviews some of those tools. Applications of these tools in assembly automation have become more widespread due to the power and affordability of present‐day computers.

Many new assembly automation applications may emerge and greater use may be made of hybrid tools that combine the strengths of two or more of the tools reviewed in the paper. The tools and methods reviewed in this paper have minimal computation complexity and can be implemented on small assembly lines, single robots or systems with low‐capability microcontrollers.

It may take another decade for engineers to recognize the benefits given the current lack of familiarity and the technical barriers associated with using these tools and it may take a long time for direct digital manufacturing to be considered commonplace… but it is expanding. The appropriate deployment of the new AI tools will contribute to the creation of more competitive assembly automation systems.

Other technological developments in AI that will impact on assembly automation include data mining, multi‐agent systems and distributed self‐organising systems.

The novel approaches proposed use ambient intelligence and the mixing of different AI tools in an effort to use the best of each technology. The concepts are generically applicable across all industrial assembly processes and this research is intended to prove that the concepts work in manufacturing.

In this paper, the authors take an amorphous flattened air-ground wireless self-assembling network system as the research object and focus on solving the wireless self-assembling network topology instability problem caused by unknown control communication faults during the operation of this system.

In the paper, the authors propose a neural network-based direct robust adaptive non-fragile fault-tolerant control algorithm suitable for the air-ground integrated wireless ad hoc network integrated system.

The simulation results show that the system eventually tends to be asymptotically stable, and the estimation error asymptotically tends to zero with the feedback adjustment of the designed controller. The system as a whole has good fault tolerance performance and autonomous learning approximation performance. The experimental results show that the wireless self-assembled network topology has good stability performance and can change flexibly and adaptively with scene changes. The stability performance of the wireless self-assembled network topology is improved by 66.7% at maximum.

The research results may lack generalisability because of the chosen research approach. Therefore, researchers are encouraged to test the proposed propositions further.

This paper designs a direct, robust, non-fragile adaptive neural network fault-tolerant controller based on the Lyapunov stability principle and neural network learning capability. By directly optimizing the feedback matrix K to approximate the robust fault-tolerant correction factor, the neural network adaptive adjustment factor enables the system as a whole to resist unknown control and communication failures during operation, thus achieving the goal of stable wireless self-assembled network topology.

This paper proposes a competence that enables the pivot organization to dynamically combine (assemble/disassemble, activate/deactivate) resources as needed, and introduces the concept of “dynamic assembly” that integrates this new competence into the historic pivot’s capacities.

Two in-depth case studies performed in two different contexts support our theoretical construct and exhibit the strategic role of the pivot-assembler in the conception, combination, coordination, and control of temporary chains and networks.

The results of our research confirm that dynamic assembly is an important characteristic of both, the travel industry and humanitarian relief, in which the four dimensions (conception, combination, coordination, and control) were found.

From this research it is possible to consider that the theoretical construct of dynamic assembly is meaningful in these types of contexts where chains are temporarily assembled from a dynamic network. Complementary research should look at the characteristics of the organizational structure and the management of competences in loosely-coupled organizations (Weick, 1982) and hastily formed networks (Denning, 2006).

This paper aims to provide a new dynamic modeling approach for root cause detection of the auto-body assembly variation.

The dynamic characteristics, such as fixture element wear and quality of incoming parts, are considered in assembly variation modeling with the dynamic Bayesian network. Based on the network structure mapping, the parameter learning of different types of nodes is conducted by integrating process knowledge and Monte Carlo simulation. The inference was that both the measurement data and maintenance actions are evidence for the improvement of diagnosis accuracy.

The proposed assembly variation model which has incorporated dynamic manufacturing features could be used to detect multiple process faults effectively.

A dynamic variation modeling method is proposed. This method could be used to provide more accurate diagnosis results and preventive maintenance guidelines for the assembly process.

Public administration has struggled to develop effective practices for fostering just and sustainable responses to social, economic, and environmental crises. In this chapter, we argue that radically democratic social movements demonstrate the potential the ideal-type of Integrative Governance holds for achieving the collaborative advantage that has remained elusive to those who study and utilize traditional governance networks. Drawing from myriad studies of social movements, we demonstrate how particular social movements prefigure the philosophy and practices of this approach. Herein we focus on movements’ ethical stance of Stewardship, politics of Radical Democracy, epistemological use of Integral Knowing, and administrative practice of Facilitative Coordination, emphasizing how they use information communication technology and one-to-one organizing tactics. These practices enable social movements to integrate across the domains of sustainability and translate radically democratic modes of association from micro- to macro-scale. Thus, they shift attention from network structures, the main focus of the governance literature, to power dynamics. These movements constitute an interconnected global phenomenon, fostering solidarity across difference and prefiguring a transformation of the global political economy. Therefore, they are nascent exemplars of Integrative Governance, a more just and effective approach to global governance.

To present a paper which examine the UK's approach to devolution in respect of the English regions.

The paper seeks to understand the policy choices facing the UK Government in redesigning governance arrangements in the English regions. This is achieved by an analysis of the evolution of regional governance arrangements in the past decade drawing on secondary and semi‐structured interviews undertaken by the author.

Regional governance arrangements evolved in an ad hoc manner due to the government's focus on the establishment of elected regional assemblies. In this policy vacuum existing regional institutions succeeded in establishing effective working relationships. However, with an increasing focus on cities as the engines of regional growth and the pressures to devolve responsibilities to local government the existing institutional policy framework has increasingly been challenges.

The paper critically examines different policy choices for reforming regional governance.

Drawing on research and consultancy studies undertaken by the author for the English Regions Network, individual Regional Assemblies, the Department of the Environment, Transport and the Regions, the Cabinet Office and the Economic and Social Research Council, the paper explores issues of policy development and implementation at the regional level.

The paper presents a comprehensive overarching analysis in a complex field of territorial public policy.