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This paper presents the outcome of research related to application of formal rules and standard procedures in EAsʼ procurement of goods and services for foreign aid-funded projects. Executing agencies are entrusted to implement foreign aid-funded projects on behalf of respective governments and they are required to satisfy a combination of rules of their multiple principals, mainly donor organizations and respective government ministries. The theoretical framework of this study is guided by agency theory. The findings indicate that the processing of procurement related information and awarding contracts by the executing agencies in the context of Bangladesh is heavily dependent on the informal working systems or “unwritten ground rules”. These are driven by downward hierarchical verbal and non-verbal instructions. The study has adopted a qualitative method following a grounded theory approach.

The purpose of this paper is to design a reasonable joining path and achieve assembly automation for multiple arc-shaped panels. A fuselage panel is primarily composed of skins, stringers, frames and clips. Both inserted and nested structures are adopted in the panels to improve the strength and hermeticity of the fuselage. Due to the complex structures and relationships, it is a challenge to coordinate the arc-shaped panels in the assembly process.

A motion sequence model which achieves arc approximation based on the relative motion of multiple panels is established. The initial position of the panels is obtained by decomposing the computer-aided design model of the panels. Two translation rules, i.e. progressively decreasing translation and limited deformation translation, are applied to determine the moving path of the panels. If a panel is not at its path node, a search algorithm is used to find the nearest path node. Finally, the key algorithms are implemented in an integration system to promote joining automation of multiple panels.

The zigzag path is effective for the joining of multiple panels with complex mating relationships. The automation of the join–separate–rejoin operations is time-saving and safety-assuring. The proposed method is demonstrated in practical engineering and a good efficiency is obtained.

This method has been used in a middle fuselage assembly project. The practical results show that the zigzag path is convenient to be stored and reused, and the synchronous movements of multiple curved panels are precisely realized. Additionally, the posture accuracy of panels is significantly improved, and the operating time is reduced considerably.

This paper gives a solution including path planning and process integration to solve the joining problem of multiple panels. The research will promote the automation of fuselage assembly.

Virtual assembly process plays an important role in assembly design of complex product and is typically time- and resource-intensive. This paper aims to investigate a dynamic assembly simplification approach in order to demonstrate and interact with virtual assembly process of complex product in real time.

The proposed approach regards the virtual assembly process of complex product as an incremental growth process of dynamic assembly. During the growth process, the current-assembled-state assembly model is simplified with appearance preserved by detecting and removing its invisible features, and the to-be-assembled components are simplified with assembly features preserved using conjugated subgraphs matching method based on an improved subgraph isomorphism algorithm.

The dynamic assembly simplification approach is applied successfully to reduce the complexity of computer aided design models during the virtual assembly process and it is proved by several cases.

A new assembly features definition is proposed based on the notion of “conjugation” to assist the assembly features recognition, which is a main step of the dynamic assembly simplification and has been translated into conjugated subgraphs matching problem. And an improved subgraph isomorphism algorithm is presented to address this problem.

The purpose of this paper is to evaluate the influence of temperature, load and sliding speed on wear and friction behavior of LM25/SiC composites in as-cast and heat-treated conditions.

The LM25/SiC aluminum matrix composites (AMCs) were prepared using the stir casting process. The wear tests were carried out using a pin-on-disc setup in dry condition. The three levels of each parameter, i.e. 100, 150 and 200°C operating temperature; 15, 25 and 35 N load; 0.8, 1.6 and 2.4 m/sec sliding speed, were considered for the investigation. ANOVA has been carried out to evaluate the percentage contribution of parameters. Scanning electron microscope analysis of worn surfaces has been carried out to understand the wear mechanism.

The wear and coefficient of friction (COF) increase with the increase in the temperature, load and sliding speed within a selected range for as-cast as well as heat-treated LM25/SiC AMCs. The mean values of wear and COF in heat-treated samples were found to be lower than as-cast samples for all cases. It was observed that the percentage wear increases significantly as temperature increases in as-cast AMCS. Mild to severe wear transition was observed at 150°C. In heat-treated AMCs, mild wear was observed irrespective of temperature. It was also observed that as the temperature increases, transition of wear mechanism from abrasive to adhesive (including delamination) occurs earlier in as-cast samples as compared to heat-treated samples.

There is a lack of data on combined effect of temperature, load and sliding speed on tribological aspects of as-cast and heat-treated LM25/SiC AMCs, limiting its applications. The present research work has addressed this gap.

Three-dimensional computer-aided design (CAD) assembly model has become important resource for design reuse in enterprises, which implicates plenty of design intent, assembly intent, design experience knowledge and functional structures. To acquire quickly CAD assembly models associated with specific functions by using product function requirement information in the product conceptual design phase for model reuse, this paper aims to find an approach for structure-function correlations analysis and functional semantic annotation of mechanical CAD assembly model before functional semantic-based assembly retrieval.

An approach for structure-function correlations analysis and functional semantic annotation of CAD assembly model is proposed. First, the product knowledge model is constructed based on ontology including design knowledge and function knowledge. Then, CAD assembly model is represented by part attributed adjacency graph and partitioned into multiple functional regions. Assembly region and flow-activity region are defined for structure-function correlations analysis of CAD assembly model. Meanwhile, the extraction process of assembly region and flow-activity region is given in detail. Furthermore, structure-function correlations analysis and functional semantic annotation are achieved by considering comprehensively assembly structure and assembled part shape structure in CAD assembly model. After that, a structure-function relation model is established based on polychromatic sets for expressing explicitly and formally relationships between functional structures, assembled parts and functional semantics.

The correlation between structure and function is analyzed effectively, and functional semantics corresponding to structures in CAD assembly model are labeled. Additionally, the relationships between functional structures, assembled parts and functional semantics can be described explicitly and formally.

The approach can be used to help designers accomplish functional semantic annotation of CAD assembly models in model repository, which provides support for functional semantic-based CAD assembly retrieval in the product conceptual design phase. These assembly models can be reused for product structure design and assembly process design.

The paper proposes a novel approach for structure-function correlations analysis and functional semantic annotation of mechanical CAD assembly model. Functional structures in assembly model are extracted and analyzed from the point of view of assembly structure and function part structure. Furthermore, the correlation relation between structures, assembled parts and functional semantics is expressed explicitly and formally based on polychromatic sets.

The purpose of the paper is to develop a method of automatic classification of the components of the assembly units. The method is crucial for developing an automatic ship assembly planning tools. The proposed method takes into account the assumptions specific for shipbuilding technology processes: high complexity of structures, difficult expert-based classification of components, fixed priority relations between connections resulting from geometrical constraints and demands of welding processes.

The set of ex post determined liaisons and assembly sequences constitutes the database of structures which have been made-up earlier. The components classification problem is solved using matrix coding of graphs. Information in such form is stored in the database. The minimization of number of cycles in the graph of classes sequence and minimization of diversity of classes within all constructions has been proposed as criteria of optimization. The genetic algorithm has been proposed as a solution method.

The proposed method solves the problem of components’ classifications. It allows setting the pattern of priorities between classes of various connections. This gives a chance to determine the relationship constraints between the connections of new structures for which assembly sequences are not established.

Mathematical formulation of the database is quite laborious. The possibility of partial automation of this process should be considered. Owing to the complexity of the problem, a relatively simple objective function has been proposed. During a ship hull assembly, additional criteria should be taken into account, what will be the direction of further research.

Automatic classification of components is dedicated for implementation in shipyards and similar assembly systems. Tests performed by the authors confirm efficiency of presented method in supporting management of the database and assembly of new structures planning. Suggested activity-oriented approach allows for easy conversion of any assembly unit structure to the form of a matrix.

The new approach for components classification according to its assembly features distinguishes the proposed method from others. The use of nilpotent matrix theory in an acyclicity of graphs analysis is also a unique achievement. Original crossover and mutation operators for assembly sequence were proposed in the article.

The purpose of the study is to establish a predictive model for sustainable wire electrical discharge machining (WEDM) by using adaptive neuro fuzzy interface system (ANFIS). Machining was done on Titanium grade 2 alloy, which is also nicknamed as workhorse of commercially pure titanium industry. ANFIS, being a state-of-the-art technology, is a highly sophisticated and reliable technique used for the prediction and decision-making.

Keeping in the mind the complex nature of WEDM along with the goal of sustainable manufacturing process, ANFIS was chosen to construct predictive models for the material removal rate (MRR) and power consumption (Pc), which reflect environmental and economic aspects. The machining parameters chosen for the machining process are pulse on-time, wire feed, wire tension, servo voltage, servo feed and peak current.

The ANFIS predicted values were verified experimentally, which gave a root mean squared error (RMSE) of 0.329 for MRR and 0.805 for Pc. The significantly low RMSE verifies the accuracy of the process.

ANFIS has been there for quite a time, but it has not been used yet for its possible application in the field of sustainable WEDM of titanium grade-2 alloy with emphasis on MRR and Pc. The novelty of the work is that a predictive model for sustainable machining of titanium grade-2 alloy has been successfully developed using ANFIS, thereby showing the reliability of this technique for the development of predictive models and decision-making for sustainable manufacturing.

The purpose of this study is to present a comprehensive review of the fundamental concepts and terminologies pertaining to different types of aluminium metal matrix composites, their joining techniques and challenges, friction stir welding (FSW) process, post-welding characterizations and basic control theory of FSW, followed by the discussions on the research reports in these areas.

Joining of aluminium metal matrix composites (Al-MMC) poses many challenges. These materials have their demanding applications in versatile domains, and hence it is essential to understand their weldability and material characteristics. FSW is a feasible choice for joining of Al-MMC over the fusion welding because of the formation of narrow heat affected zone and minimizing the formation of intermetallic compounds at weld interface. The goal in FSW is to generate enough thermal energy by friction between the workpiece and rotating tool. Heat energy is generated by mechanical interaction because of the difference in velocity between the workpiece and rotating tool. In the present work, a detailed survey is done on the above topics and an organised conceptual context is presented. A complete discussion on significance of FSW process parameters, control schemes, parameter optimization and weld quality monitoring are presented, along with the analysis on relation between the interdependent parameters.

Results from the study present the research gaps in the FSW studies for joining of the aluminium-based metal matrix composites, and they highlight further scope of studies pertaining to this domain.

It is observed that the survey done on FSW of Al-MMCs and their control theory give an insight into the fundamental concepts pertaining to this research area to enhance interdisciplinary technology exploration.

The use of AI for operations management, with its ability to evolve solutions, handle uncertainty and perform optimisation continues to be a major field of research. The growing body of publications over the last two decades means that it can be difficult to keep track of what has been done previously, what has worked, and what really needs to be addressed. Hence, the purpose of this paper is to present a survey of the use of AI in operations management aimed at presenting the key research themes, trends and directions of research.

The paper builds upon our previous survey of this field which was carried out for the ten‐year period 1995‐2004. Like the previous survey, it uses Elsevier's Science Direct database as a source. The framework and methodology adopted for the survey is kept as similar as possible to enable continuity and comparison of trends. Thus, the application categories adopted are: design; scheduling; process planning and control; and quality, maintenance and fault diagnosis. Research on utilising neural networks, case‐based reasoning (CBR), fuzzy logic (FL), knowledge‐Based systems (KBS), data mining, and hybrid AI in the four application areas are identified.

The survey categorises over 1,400 papers, identifying the uses of AI in the four categories of operations management and concludes with an analysis of the trends, gaps and directions for future research. The findings include: the trends for design and scheduling show a dramatic increase in the use of genetic algorithms since 2003 that reflect recognition of their success in these areas; there is a significant decline in research on use of KBS, reflecting their transition into practice; there is an increasing trend in the use of FL in quality, maintenance and fault diagnosis; and there are surprising gaps in the use of CBR and hybrid methods in operations management that offer opportunities for future research.

This is the largest and most comprehensive study to classify research on the use of AI in operations management to date. The survey and trends identified provide a useful reference point and directions for future research.

The information and knowledge about a product and its assembly are necessary to generate all feasible assembly sequences of that product. Assemblies contain a very large amount of information and complex relationships. Identifying assembled parts as well as their contact surfaces is very important in design and manufacturing since this information is essential. The problem is to not only make the information available but also use the relevant information for making decisions, especially determination of the optimum assembly sequence. This paper aims to address these issues.

This paper describes a system for processing assembly models and extracting assembly related data using application programming interface (API) of the computer‐aided design (CAD) software. These data are used to identify the relationships between different components of an assembly thus encouraging generation of feasible assembly sequences.

Instead of total human interpretation of the assembly design, a direct CAD database interface approach has been proposed to extract the relation with minimal manual involvement. The information extracted is used to generate a list describing the links between the assembled parts, the involved features and the type of link explicitly to facilitate assembly analysis and planning.

The methodology of using the API of the CAD modeling package SolidWorks, is a novel approach in which the assembly mate information is captured. Instead of total human interpretation of the assembly design, a direct CAD database interface approach has been proposed to extract the relation with minimal manual involvement.