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      Digital Twin and Virtual Reality Based Methodology for Multi-Robot Manufacturing Cell Commissioning

      , , , ,
      Applied Sciences
      MDPI AG

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          Abstract

          Intelligent automation, including robotics, is one of the current trends in the manufacturing industry in the context of “Industry 4.0”, where cyber-physical systems control the production at automated or semi-automated factories. Robots are perfect substitutes for a skilled workforce for some repeatable, general, and strategically-important tasks. However, this transformation is not always feasible and immediate, since certain technologies do not provide the required degree of flexibility. The introduction of collaborative robots in the industry permits the combination of the advantages of manual and automated production. In some processes, it is necessary to incorporate robots from different manufacturers, thus the design of these multi-robot systems is crucial to guarantee the maximum quality and efficiency. In this context, this paper presents a novel methodology for process automation design, enhanced implementation, and real-time monitoring in operation based on creating a digital twin of the manufacturing process with an immersive virtual reality interface to be used as a virtual testbed before the physical implementation. Moreover, it can be efficiently used for operator training, real-time monitoring, and feasibility studies of future optimizations. It has been validated in a use case which provides a solution for an assembly manufacturing process.

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          A Cyber-Physical Systems architecture for Industry 4.0-based manufacturing systems

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            Digital twin-driven product design, manufacturing and service with big data

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              About The Importance of Autonomy and Digital Twins for the Future of Manufacturing

                Author and article information

                Contributors
                (View ORCID Profile)
                (View ORCID Profile)
                Journal
                ASPCC7
                Applied Sciences
                Applied Sciences
                MDPI AG
                2076-3417
                May 2020
                May 24 2020
                : 10
                : 10
                : 3633
                Article
                10.3390/app10103633
                f8126827-9ae6-4227-bcef-32a6f34a3290
                © 2020

                https://creativecommons.org/licenses/by/4.0/

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