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      Digital Twin for Monitoring Ergonomics during Manufacturing Production

      , , ,
      Applied Sciences
      MDPI AG

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          Abstract

          Within the era of smart factories, concerning the ergonomics related to production processes, the Digital Twin (DT) is the key to set up novel models for monitoring the performance of manual work activities, which are able to provide results in near real time and to support the decision-making process for improving the working conditions. This paper aims to propose a methodological framework that, by implementing a human DT, and supports the monitoring and the decision making regarding the ergonomics performances of manual production lines. A case study, carried out in a laboratory, is presented for demonstrating the applicability and the effectiveness of the proposed framework. The results show how it is possible to identify the operational issues of a manual workstation and how it is possible to propose and test improving solutions.

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          Most cited references28

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          The Internet of Things: A survey

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            The design of manual handling tasks: revised tables of maximum acceptable weights and forces.

            Four new manual handling experiments are reviewed. The experiment used male and female subjects to study lifting, lowering, pushing, pulling, and carrying tasks. Each experiment used a psychophysical methodology with measurements of oxygen consumption, heart rate, and anthropometric characteristics. Independent variables included task frequency, distance, height and duration; object size and handles; extended horizontal reach; and combination tasks. The results of the four experiments were integrated with the results of seven similar experiments published previously by this laboratory. The integrated data were used to revise maximum acceptable weights and forces originally published in 1978. The revised tables are presented and compared with the original tables.
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              An application framework of digital twin and its case study

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                Author and article information

                Contributors
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                Journal
                ASPCC7
                Applied Sciences
                Applied Sciences
                MDPI AG
                2076-3417
                November 2020
                November 02 2020
                : 10
                : 21
                : 7758
                Article
                10.3390/app10217758
                0aed93cb-bf40-4239-9084-2679ced469b9
                © 2020

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

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