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  • 1.
    Amouzgar, Kaveh
    et al.
    University of Skövde, School of Engineering Science. University of Skövde, The Virtual Systems Research Centre.
    Bandaru, Sunith
    University of Skövde, School of Engineering Science. University of Skövde, The Virtual Systems Research Centre.
    Andersson, Tobias J.
    University of Skövde, School of Engineering Science. 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.
    A framework for simulation based multi-objective optimization and knowledge discovery of machining process2018In: The International Journal of Advanced Manufacturing Technology, ISSN 0268-3768, E-ISSN 1433-3015, Vol. 98, no 9-12, p. 2469-2486Article in journal (Refereed)
  • 2.
    De Vin, Leo
    et al.
    University of Skövde, School of Technology and Society.
    Ng, Amos H. C.
    University of Skövde, School of Technology and Society.
    Sundberg, Martin
    University of Skövde, School of Technology and Society.
    Moore, Philip R.
    De Montfort Univ, Mechatron Res Ctr, Leicester LE1 9BH, Leics, England.
    Pu, Junsheng
    De Montfort Univ, Mechatron Res Ctr, Leicester LE1 9BH, Leics, England.
    Wong, Bill C.-B.
    De Montfort Univ, Mechatron Res Ctr, Leicester LE1 9BH, Leics, England.
    Information fusion for decision support in manufacturing: studies from the defense sector2008In: The International Journal of Advanced Manufacturing Technology, ISSN 0268-3768, E-ISSN 1433-3015, Vol. 35, no 9-10, p. 908-915Article in journal (Other academic)
    Abstract [en]

    Information fusion, the synergistic combination of information from multiple sources, is an established research area within the defense sector. In manufacturing however, it is less well-established, with the exception of sensor/data fusion for automatic decision making. The paper briefly discusses some military specific models and methods for information fusion; analogies with manufacturing as well as a more generalized terminology are presented. “Manufacturing” is an application scenario within a Swedish information fusion research program that studies information fusion from databases, sensors and simulations with (currently) a focus on support for human decision making. An area of particular interest is that of advanced applications of virtual manufacturing such as synthetic environments, a form of hardware in the loop simulation that can deliver services such as service and maintenance at remote locations. In this area, the manufacturing industry can benefit from ongoing work in the defense sector related to verification, validation and accreditation of simulation models.

  • 3.
    Schmidt, Bernard
    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.
    Cloud-enhanced predictive maintenance2018In: The International Journal of Advanced Manufacturing Technology, ISSN 0268-3768, E-ISSN 1433-3015, Vol. 99, no 1-4, p. 5-13Article in journal (Refereed)
    Abstract [en]

    Maintenance of assembly and manufacturing equipment is crucial to ensure productivity, product quality, on-time delivery, and a safe working environment. Predictive maintenance is an approach that utilises the condition monitoring data to predict the future machine conditions and makes decisions upon this prediction. The main aim of the present research is to achieve an improvement in predictive condition-based maintenance decision making through a cloud-based approach with usage of wide information content. For the improvement, it is crucial to identify and track not only condition related data but also context data. Context data allows better utilisation of condition monitoring data as well as analysis based on a machine population. The objective of this paper is to outline the first steps of a framework and methodology to handle and process maintenance, production, and factory related data from the first lifecycle phase to the operation and maintenance phase. Initial case study aims to validate the work in the context of real industrial applications.

  • 4.
    Wang, Lihui
    et al.
    University of Skövde, School of Engineering Science. University of Skövde, The Virtual Systems Research Centre. Department of Production Engineering, Royal Institute of Technology, Stockholm, Sweden.
    Schmidt, Bernard
    University of Skövde, School of Engineering Science. University of Skövde, The Virtual Systems Research Centre.
    Givehchi, Mohammad
    Department of Production Engineering, Royal Institute of Technology, Stockholm, Sweden.
    Adamson, Göran
    University of Skövde, School of Engineering Science. University of Skövde, The Virtual Systems Research Centre.
    Robotic Assembly Planning and Control with Enhanced Adaptability through Function Blocks2015In: The International Journal of Advanced Manufacturing Technology, ISSN 0268-3768, E-ISSN 1433-3015, Vol. 77, no 1-4, p. 705-715Article in journal (Refereed)
    Abstract [en]

    Manufacturing companies today need to maintain a high level of flexibility and adaptability to deal with uncertainties on dynamic shop floors, including e.g. cutting tool shortage, part supply interruption, urgent job insertion or delay, and machine unavailability. Such uncertainties are characteristic in component assembly operations. Addressing the problem, we propose a new method using function blocks to achieve much improved adaptability in assembly planning and robot control. In this paper, we propose to use event-driven function blocks for robotic assembly, aiming to plan trajectory and execute assembly tasks in real-time. It is envisioned that this approach will achieve better adaptability if applied to real-world applications.

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