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  • 1.
    Nikolakis, Nikolaos
    et al.
    Laboratory for Manufacturing Systems and Automation, Department of Mechanical Engineering and Aeronautics, University of Patras, Greece.
    Senington, Richard
    University of Skövde, School of Engineering Science. University of Skövde, Virtual Engineering Research Environment.
    Sipsas, Konstantinos
    Laboratory for Manufacturing Systems and Automation, Department of Mechanical Engineering and Aeronautics, University of Patras, Greece.
    Syberfeldt, Anna
    University of Skövde, School of Engineering Science. University of Skövde, Virtual Engineering Research Environment.
    Makris, Sotiris
    Laboratory for Manufacturing Systems and Automation, Department of Mechanical Engineering and Aeronautics, University of Patras, Greece.
    On a containerized approach for the dynamic planning and control of a cyber - physical production system2020In: Robotics and Computer-Integrated Manufacturing, ISSN 0736-5845, E-ISSN 1879-2537, Vol. 64, article id 101919Article in journal (Refereed)
    Abstract [en]

    The increased complexity of modern production systems requires sophisticated system control approaches to maintain high levels of flexibility. Furthermore, the request for customized production with the introduction of heterogeneous production resources, increases the diversity of manufacturing systems making their reconfiguration complex and time consuming. In this paper, an end-to-end approach for reconfigurable cyber-physical production systems is discussed, enabled by container technologies. The presented approach enhances flexibility in a cyber-physical production system (CPPS) through the dynamic reconfiguration of the automation system and the production schedule, based on occurring events. High-level management of manufacturing operations is performed on a centralized node while the data processing and execution control is handled at the network edge. Runtime events are generated at the edge and in smart connected devices via means of a variant of IEC61499 function blocks. Software containers manage the deployment and low-level orchestration of FBs at the edge devices. All aspects of the proposed solution have been implemented on a software framework and applied in a small scale CPPS coming from the automotive industry.

  • 2.
    Strand, Mattias
    et al.
    University of Skövde, School of Engineering Science. University of Skövde, Virtual Engineering Research Environment.
    Syberfeldt, Anna
    University of Skövde, School of Engineering Science. University of Skövde, Virtual Engineering Research Environment.
    Using external data in a BI solution to optimise waste management2020In: Journal of Decision Systems, ISSN 1246-0125, E-ISSN 2116-7052Article in journal (Refereed)
    Abstract [en]

    BI solutions are constantly being developed to support decision-making at various organisational levels. These solutions facilitate the compilation, aggregation and summarisation of large volumes of data. Consequently, the business value created by these systems is increasing as they sustain more and more advanced analytics, ranging from descriptive analytics, to predictive analytics, to prescriptive analytics. However, most organisations work primarily with internal data. Despite many references in the literature to the value hidden in external data, details on how such data can be used are scarce. In this paper, we present the results of an extensive action case study at a public waste management company. The results illustrate how external data from several external data sources, integrated into an up-and-running BI solution, are used jointly to allow for descriptive and predictive analytics, as well as prescriptive analytics. In addition, details of these analytical values are given and related to organisational benefits.

  • 3.
    Syberfeldt, Anna
    et al.
    University of Skövde, School of Engineering Science. University of Skövde, Virtual Engineering Research Environment.
    Ekblom, Tom
    University of Skövde, School of Engineering Science. University of Skövde, Virtual Engineering Research Environment.
    Improved Automatic Quality Inspections through the Integration of State-of-the-Art Machine Vision and Collaborative Robots2019In: Advances in Manufacturing Technology XXXIII: Proceedings of the 17th International Conference on Manufacturing Research, incorporating the 34th National Conference on Manufacturing Research, September 10–12, 2019, Queen’s University Belfast, UK / [ed] Yan Jin, Mark Price, Amsterdam: IOS Press, 2019, Vol. 9, p. 107-112Conference paper (Refereed)
    Abstract [en]

    In this paper, we discuss the concepts of a flexible and high-performing solution for automatic quality control that integrates state-of-the-art machine learning algorithms with collaborative robots. The overall aim of the paper is to take the first steps towards improved automatic quality inspections in the manufacturing industry, leading to reduced quality defects and reduced costs in the manufacturing process. For developing and evaluating a first version of a solution that integrates state-of-the-art machine vision and collaborative robots we use a real-world case study focusing on improved quality inspection. Results from the case study shows that it is possible to realize automatic quality inspections through the use of a collaborative robot as intended, but also that there are some challenges that need to be further addressed in order to achieve a top-performing system.

  • 4.
    Yan, Xiu-Tian
    et al.
    Design, Manufacture and Engineering Management, University of Strathclyde, Glasgow, UK.
    Bradley, DavidDundee, UK.Russell, DavidMalvern, USA.Moore, PhilipUniversity of Skövde, School of Engineering Science. University of Skövde, Virtual Engineering Research Environment.
    Reinventing Mechatronics: Developing Future Directions for Mechatronics2020Conference proceedings (editor) (Refereed)
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