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  • 1.
    Adamson, Göran
    et al.
    University of Skövde, School of Engineering Science. University of Skövde, The Virtual Systems Research Centre.
    Wang, Lihui
    University of Skövde, School of Engineering Science. University of Skövde, The Virtual Systems Research Centre. Department of Production Engineering, KTH Royal Institute of Technology, Stockholm, Sweden.
    Holm, Magnus
    University of Skövde, School of Engineering Science. University of Skövde, The Virtual Systems Research Centre.
    Moore, Philip
    Academy for Innovation & Research, Falmouth University, Cornwall, United Kingdom.
    Cloud Manufacturing: A Critical Review of Recent Development and Future Trends2017In: International journal of computer integrated manufacturing (Print), ISSN 0951-192X, E-ISSN 1362-3052, Vol. 30, no 4-5, p. 347-380Article in journal (Refereed)
    Abstract [en]

    There is an on-going paradigm shift in manufacturing, in which modern manufacturing industry is changing towards global manufacturing networks and supply chains. This will lead to the flexible usage of different globally distributed, scalable and sustainable, service-oriented manufacturing systems and resources. Combining recently emerged technologies, such as Internet of Things, Cloud Computing, Semantic Web, service-oriented technologies, virtualisation and advanced high-performance computing technologies, with advanced manufacturing models and information technologies, Cloud Manufacturing is a new manufacturing paradigm built on resource sharing, supporting and driving this change.

    It is envisioned that companies in all sectors of manufacturing will be able to package their resources and know-hows in the Cloud, making them conveniently available for others through pay-as-you-go, which is also timely and economically attractive. Resources, e.g. manufacturing software tools, applications, knowledge and fabrication capabilities and equipment, will then be made accessible to presumptive consumers on a worldwide basis.

    Cloud Manufacturing has been in focus for a great deal of research interest and suggested applications during recent years, by both industrial and academic communities. After surveying a vast array of available publications, this paper presents an up-to-date literature review together with identified outstanding research issues, and future trends and directions within Cloud Manufacturing.

  • 2.
    Dudas, Catarina
    et al.
    University of Skövde, The Virtual Systems Research Centre.
    Ng, Amos H.C.
    University of Skövde, School of Engineering Science. University of Skövde, The Virtual Systems Research Centre.
    Pehrsson, Leif
    University of Skövde, School of Engineering Science. University of Skövde, The Virtual Systems Research Centre.
    Boström, Henrik
    Department of Computer and Systems Sciences, Stockholm University, Stockholm, Sweden.
    Integration of data mining and multi-objective optimisation for decision support in production system development2014In: International journal of computer integrated manufacturing (Print), ISSN 0951-192X, E-ISSN 1362-3052, Vol. 27, no 9, p. 824-839Article in journal (Refereed)
    Abstract [en]

    Multi-objective optimisation (MOO) is a powerful approach for generating a set of optimal trade-off (Pareto) design alternatives that the decision-maker can evaluate and then choose the most-suitable configuration, based on some high-level strategic information. Nevertheless, in practice, choosing among a large number of solutions on the Pareto front is often a daunting task, if proper analysis and visualisation techniques are not applied. Recent research advancements have shown the advantages of using data mining techniques to automate the post-optimality analysis of Pareto-optimal solutions for engineering design problems. Nonetheless, it is argued that the existing approaches are inadequate for generating high-quality results, when the set of the Pareto solutions is relatively small and the solutions close to the Pareto front have almost the same attributes as the Pareto-optimal solutions, of which both are commonly found in many real-world system problems. The aim of this paper is therefore to propose a distance-based data mining approach for the solution sets generated from simulation-based optimisation, in order to address these issues. Such an integrated data mining and MOO procedure is illustrated with the results of an industrial cost optimisation case study. Particular emphasis is paid to showing how the proposed procedure can be used to assist decision-makers in analysing and visualising the attributes of the design alternatives in different regions of the objective space, so that informed decisions can be made in production systems development.

  • 3.
    Mohammed, Abdullah
    et al.
    Department of Production Engineering, KTH Royal Institute of Technology, Stockholm, Sweden.
    Schmidt, Bernard
    University of Skövde, School of Engineering Science. University of Skövde, The Virtual Systems Research Centre.
    Wang, Lihui
    Department of Production Engineering, KTH Royal Institute of Technology, Stockholm, Sweden.
    Active collision avoidance for human-robot collaboration driven by vision sensors2017In: International journal of computer integrated manufacturing (Print), ISSN 0951-192X, E-ISSN 1362-3052, Vol. 30, no 9, p. 970-980Article in journal (Refereed)
    Abstract [en]

    Establishing safe human-robot collaboration is an essential factor for improving efficiency and flexibility in today's manufacturing environment. Targeting safety in human-robot collaboration, this paper reports a novel approach for effective online collision avoidance in an augmented environment, where virtual three-dimensional (3D) models of robots and real images of human operators from depth cameras are used for monitoring and collision detection. A prototype system is developed and linked to industrial robot controllers for adaptive robot control, without the need of programming by the operators. The result of collision detection reveals four safety strategies: the system can alert an operator, stop a robot, move away the robot, or modify the robot's trajectory away from an approaching operator. These strategies can be activated based on the operator's existence and location with respect to the robot. The case study of the research further discusses the possibility of implementing the developed method in realistic applications, for example, collaboration between robots and humans in an assembly line.

  • 4.
    Prasoon, Ruchir
    et al.
    Department of Mechanical Engineering, Indian Institute of Technology, Kharagpur, 721302, W Bengal, India.
    Das, Devashish
    Department of Mechanical Engineering, Indian Institute of Technology, Kharagpur, 721302, W Bengal, India.
    Tiwari, Manoj Kumar
    Department of Industrial Engineering, Indian Institute of Technology, Kharagpur, 721302, W Bengal, India.
    Wang, Lihui
    University of Skövde, The Virtual Systems Research Centre. University of Skövde, School of Technology and Society.
    An algorithm portfolio approach to reconfigurable set-up planning2011In: International journal of computer integrated manufacturing (Print), ISSN 0951-192X, E-ISSN 1362-3052, Vol. 24, no 8, p. 756-768Article in journal (Refereed)
    Abstract [en]

    This article discusses an algorithm portfolio approach to find optimal set-up plans in a dynamic shop floor environment where flexibility and promptness of the decision process is critical along with best possible utilisation of the available resources. An evolutionary algorithm based reconfigurable set-up planning approach is presented where the final set-up plan is determined in two steps: primitive set-up planning through feature grouping and reconfigurable set-up merging based on real time information from the scheduling system. The tendency of single algorithm approach to converge to sub-optimal solutions was countered by using portfolios of genetic algorithm and its three variants: Genetic Algorithm with Chromosome Differentiation, Sexual Genetic Algorithm and a modified version of Age Genetic Algorithm. Best performing portfolios selected after exhaustive experimentation showed dramatic computational improvements in achieving the optimal solution validating the appropriateness and effectiveness of algorithm portfolio approach.

  • 5.
    Schmidt, Bernard
    et al.
    University of Skövde, School of Engineering Science. University of Skövde, The Virtual Systems Research Centre.
    Gandhi, Kanika
    University of Skövde, School of Engineering Science. University of Skövde, The Virtual Systems Research Centre.
    Wang, Lihui
    University of Skövde, School of Engineering Science. University of Skövde, The Virtual Systems Research Centre. KTH Royal Institute of Technology, Stockholm, Sweden.
    Ng, Amos H. C.
    University of Skövde, School of Engineering Science. University of Skövde, The Virtual Systems Research Centre.
    Integration of events and offline measurement data from a population of similar entities for condition monitoringIn: International journal of computer integrated manufacturing (Print), ISSN 0951-192X, E-ISSN 1362-3052Article in journal (Refereed)
    Abstract [en]

    In this paper, an approach for integration of data from different sources and from a population of similar monitored entities is presented with evaluation procedure based on multiple machine learning methods that allows selection of a proper combination of methods for data integration and feature selection. It is exemplified on the real-world case from manufacturing industry with application to double ball-bar measurement from a population of machine tools. Historical data from the period of four years from a population of 29 similar multitask machine tools are analysed. Several feature selection methods are evaluated. Finally, simple economic evaluation is presented with application to proposed condition based approach. With assumed parameters, potential improvement in long term of 6 times reduced amount of unplanned stops and 40% reduced cost has been indicated with respect to optimal time based replacement policy.

  • 6.
    Thorvald, Peter
    et al.
    University of Skövde, The Virtual Systems Research Centre. University of Skövde, School of Engineering Science.
    Högberg, Dan
    University of Skövde, The Virtual Systems Research Centre. University of Skövde, School of Engineering Science.
    Case, Keith
    University of Skövde, The Virtual Systems Research Centre. University of Skövde, School of Engineering Science. Mechanical and Manufacturing Engineering, Loughborough University, Loughborough, United Kingdom.
    The effect of information mobility on production quality2014In: International journal of computer integrated manufacturing (Print), ISSN 0951-192X, E-ISSN 1362-3052, Vol. 27, no 2, p. 120-128Article in journal (Refereed)
    Abstract [en]

    This article investigates the use of a hand-held unit as an information source in manual assembly. Having a mobile information system, such as a Personal Digital Assistant (PDA), that can be brought at all times, as opposed to a stationary one, such as a computer terminal, is hypothesised to increase the information range and thus improves assembly performance. The increased information range is argued to be due to assembly workers employing a cost-benefit strategy, where the cost of gathering information is compared with the assumed benefit of it. This article reports empirical data comparing the use of a mobile information carrier with a traditional stationary computer, and results show that the use of a PDA significantly improves quality, whereas productivity does not significantly improve quality. © 2013 Copyright Taylor & Francis.

  • 7.
    Wang, Lihui
    University of Skövde, The Virtual Systems Research Centre. University of Skövde, School of Technology and Society.
    Planning towards enhanced adaptability in digital manufacturing2011In: International journal of computer integrated manufacturing (Print), ISSN 0951-192X, E-ISSN 1362-3052, Vol. 24, no 5, p. 378-390Article in journal (Refereed)
    Abstract [en]

    This paper presents an integrated approach for developing a web-based system with enhanced adaptability, including distributed process planning, real-time monitoring and remote machining. The objective is to develop a new methodology and relevant processing algorithms for enhancing adaptability in digital manufacturing. This approach is enabled by a Wise-ShopFloor (Web-based integrated sensor-driven e-ShopFloor) framework targeting distributed yet collaborative manufacturing environments. Utilising the latest Java technologies (Java 3D and Java Servlet) for system implementation, it allows end-users to plan and control distant manufacturing operations based on runtime information from shop floors. Details on the principle of the Wise-ShopFloor framework, system architecture, and a prototype system are reported in this paper. An example of distributed process planning for remote machining is chosen as a case study to demonstrate the effectiveness of this approach toward web-based digital manufacturing.

  • 8.
    Wang, Lihui
    et al.
    KTH Royal Institute of Technology, Stockholm, Sweden.
    Mohammed, Abdullah
    KTH Royal Institute of Technology, Stockholm, Sweden.
    Wang, Xi Vincent
    KTH Royal Institute of Technology, Stockholm, Sweden.
    Schmidt, Bernard
    University of Skövde, School of Engineering Science. University of Skövde, The Virtual Systems Research Centre.
    Energy-efficient robot applications towards sustainable manufacturing2018In: International journal of computer integrated manufacturing (Print), ISSN 0951-192X, E-ISSN 1362-3052, Vol. 31, no 8, p. 692-700Article in journal (Refereed)
    Abstract [en]

    The cloud technology provides sustainable solutions to the modern industrial robotic cells. Within the context, the objective of this research is to minimise the energy consumption of robots during assembly in a cloud environment. Given a robot path and based on the inverse kinematics and dynamics of the robot from the cloud, a set of feasible configurations of the robot can be derived, followed by calculating the desirable forces and torques on the joints and links of the robot. Energy consumption is then calculated for each feasible configuration along the path. The ones with the lowest energy consumption are chosen. Since the energy-efficient robot configurations lead to reduced overall energy consumption, this approach becomes instrumental and can be applied to energy-efficient robotic assembly. This cloud-based energy-efficient approach for robotic applications can largely enhance the current practice as demonstrated by the results of three case studies, leading towards sustainable manufacturing.

  • 9.
    Xu, Xun
    et al.
    Univ Auckland, Dept Mech Engn, Sch Engn, Auckland 1142, New Zealand.
    Wang, Lihui
    University of Skövde, The Virtual Systems Research Centre. University of Skövde, School of Technology and Society.
    Newman, Stephen T.
    Univ Bath, Dept Mech Engn, Bath BA2 7AY, Avon, England.
    Computer-aided process planning: A critical review of recent developments and future trends2011In: International journal of computer integrated manufacturing (Print), ISSN 0951-192X, E-ISSN 1362-3052, Vol. 24, no 1, p. 1-31Article, review/survey (Refereed)
    Abstract [en]

    For the past three decades, computer-aided process planning (CAPP) has attracted a large amount of research interest. A huge volume of literature has been published on this subject. Today, CAPP research faces new challenges owing to the dynamic markets and business globalisation. Thus, there is an urgent need to ascertain the current status and identify future trends of CAPP. Covering articles published on the subjects of CAPP in the past 10 years or so, this article aims to provide an up-to-date review of the CAPP research works, a critical analysis of journals that publish CAPP research works, and an understanding of the future direction in the field. First, general information is provided on CAPP. The past reviews are summarised. Discussions about the recent CAPP research are presented in a number of categories, i.e. feature-based technologies, knowledge-based systems, artificial neural networks, genetic algorithms, fuzzy set theory and fuzzy logic, Petri nets, agent-based technology, Internet-based technology, STEP-compliant CAPP and other emerging technologies. Research on some specific aspects of CAPP is also provided. Discussions and analysis of the methods are then presented based on the data gathered from the Elsevier’s Scopus abstract and citation database. The concepts of ‘Subject Strength’ of a journal and ‘technology impact factor’ are introduced and used for discussions based on the publication data. The former is used to gauge the level of focus of a journal on a particular research subject/domain, whereas the latter is used to assess the level of impact of a particular technology, in terms of citation counts. Finally, a discussion on the future development is presented.

1 - 9 of 9
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