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Sustainable and flexible industrial human machine interfaces to support adaptable applications in the Industry 4.0 paradigm

Author

Listed:
  • Aitor Ardanza
  • Aitor Moreno
  • Álvaro Segura
  • Mikel de la Cruz
  • Daniel Aguinaga

Abstract

The Industry 4.0 paradigm introduced several technologies in the industrial manufacturing landscape, highlighting the Cyber Physical Systems, Internet of Things and Visual Computing as some of the key enabling technologies. The fast paced evolution of the technology has to be reflected in the common ground between the machines and the operators. The introduction of advanced, flexible and adaptable Human Machine Interfaces (HMI) will empower the operators with information and custom tools to be more productive in a safer way. This work presents totally functional hardware and a novel software architecture to build flexible advanced HMI interfaces that will provide adaptable and useful information to the operator of the machines. Industrial protocols are used to receive real-time data. A USB camera can be plugged to enable the utilisation of Computer Vision algorithms. A touch display shows adaptable interfaces composed of (a) 3D interactive graphics, (b) texts and 2D images, (c) the camera feed and (d) user interface controls to enable the interaction with the operators. The results present three use cases: (a) an HMI customised for 3D printers, (b) real-time motor control from the HMI and (c) a digital twin of a robotic arm receiving real-time data from the actual robotic arm. This work present data regarding consumption of the hardware for each use case under different work-load.

Suggested Citation

  • Aitor Ardanza & Aitor Moreno & Álvaro Segura & Mikel de la Cruz & Daniel Aguinaga, 2019. "Sustainable and flexible industrial human machine interfaces to support adaptable applications in the Industry 4.0 paradigm," International Journal of Production Research, Taylor & Francis Journals, vol. 57(12), pages 4045-4059, June.
    • Handle: RePEc:taf:tprsxx:v:57:y:2019:i:12:p:4045-4059
    DOI: 10.1080/00207543.2019.1572932
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    Citations

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    Cited by:

    1. Marco Bettiol & Mauro Capestro & Eleonora Di Maria & Roberto Ganau, 2024. "Is this time different? How Industry 4.0 affects firms’ labor productivity," Small Business Economics, Springer, vol. 62(4), pages 1449-1467, April.
    2. Nguyen, Tiep & Duong, Quang Huy & Nguyen, Truong Van & Zhu, You & Zhou, Li, 2022. "Knowledge mapping of digital twin and physical internet in Supply Chain Management: A systematic literature review," International Journal of Production Economics, Elsevier, vol. 244(C).
    3. Sony, Michael & Antony, Jiju & Mc Dermott, Olivia & Garza-Reyes, Jose Arturo, 2021. "An empirical examination of benefits, challenges, and critical success factors of industry 4.0 in manufacturing and service sector," Technology in Society, Elsevier, vol. 67(C).
    4. Tortorella, Guilherme Luz & Saurin, Tarcisio A. & Hines, Peter & Antony, Jiju & Samson, Daniel, 2023. "Myths and facts of industry 4.0," International Journal of Production Economics, Elsevier, vol. 255(C).
    5. Szymon Cyfert & Waldemar Glabiszewski & Maciej Zastempowski, 2021. "Impact of Management Tools Supporting Industry 4.0 on the Importance of CSR during COVID-19. Generation Z," Energies, MDPI, vol. 14(6), pages 1-13, March.
    6. Krzysztof Ejsmont & Bartlomiej Gladysz & Aldona Kluczek, 2020. "Impact of Industry 4.0 on Sustainability—Bibliometric Literature Review," Sustainability, MDPI, vol. 12(14), pages 1-29, July.
    7. José Salvador da Motta Reis & Maximilian Espuny & Thaís Vieira Nunhes & Nilo Antonio de Souza Sampaio & Raine Isaksson & Fernando Celso de Campos & Otávio José de Oliveira, 2021. "Striding towards Sustainability: A Framework to Overcome Challenges and Explore Opportunities through Industry 4.0," Sustainability, MDPI, vol. 13(9), pages 1-28, May.
    8. Ciprian Mihai Coman & Adriana Florescu & Constantin Daniel Oancea, 2021. "Assessment of Energy Use Based on an Implementation of IoT, Cloud Systems, and Artificial Intelligence," Energies, MDPI, vol. 14(11), pages 1-21, May.
    9. Bag, Surajit & Pretorius, Jan Ham Christiaan & Gupta, Shivam & Dwivedi, Yogesh K., 2021. "Role of institutional pressures and resources in the adoption of big data analytics powered artificial intelligence, sustainable manufacturing practices and circular economy capabilities," Technological Forecasting and Social Change, Elsevier, vol. 163(C).
    10. Héctor Cañas & Josefa Mula & Francisco Campuzano-Bolarín, 2020. "A General Outline of a Sustainable Supply Chain 4.0," Sustainability, MDPI, vol. 12(19), pages 1-17, September.
    11. Kendrik Yan Hong Lim & Pai Zheng & Chun-Hsien Chen, 2020. "A state-of-the-art survey of Digital Twin: techniques, engineering product lifecycle management and business innovation perspectives," Journal of Intelligent Manufacturing, Springer, vol. 31(6), pages 1313-1337, August.

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