IDEAS home Printed from https://ideas.repec.org/a/spr/joinma/v33y2022i8d10.1007_s10845-021-01782-3.html
   My bibliography  Save this article

Current status, challenges and opportunities of sustainable ultra-precision manufacturing

Author

Listed:
  • Wai Sze Yip

    (The Hong Kong Polytechnic University)

  • Suet To

    (The Hong Kong Polytechnic University)

  • Hongting Zhou

    (The Hong Kong Polytechnic University)

Abstract

Ultra-precision manufacturing (UPM) is a promising manufacturing technology for fabricating micro-components and its production volume raises in the coming future due to a significant increase in the production volume for highly technological products nowadays. Therefore, UPM industries are accountable for executing sustainability practices to minimize negative environmental impacts from their manufacturing activities. However, sustainable UPM is difficult to execute practically up to now due to different aspects such as technology and knowledge gap. With a high speed of technology advancement nowadays, UPM industries enable to leverage this technological chance and employ the Internet of Things (IoT) technique to move UPM toward sustainability. Therefore, in this article, the current status and future perspective of sustainable UPM, the major research and technological gap between UPM and sustainability development, specific technical challenges for integrated IoT to UPM for sustainability goal are discussed and revealed to promote sustainable UPM. And consequently, a preliminary framework of IoT based UPM system with particular suggestions was firstly presented for facilitating sustainable UPM and acts as the reference to related industries and academia for further developing this novel technique in the future.

Suggested Citation

  • Wai Sze Yip & Suet To & Hongting Zhou, 2022. "Current status, challenges and opportunities of sustainable ultra-precision manufacturing," Journal of Intelligent Manufacturing, Springer, vol. 33(8), pages 2193-2205, December.
    • Handle: RePEc:spr:joinma:v:33:y:2022:i:8:d:10.1007_s10845-021-01782-3
    DOI: 10.1007/s10845-021-01782-3
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1007/s10845-021-01782-3
    File Function: Abstract
    Download Restriction: Access to the full text of the articles in this series is restricted.
    File URL: https://libkey.io/10.1007/s10845-021-01782-3?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Yingfeng Zhang & Geng Zhang & Yang Liu & Di Hu, 2017. "Research on services encapsulation and virtualization access model of machine for cloud manufacturing," Journal of Intelligent Manufacturing, Springer, vol. 28(5), pages 1109-1123, June.
    2. Pai Zheng & Xun Xu & Chun-Hsien Chen, 2020. "A data-driven cyber-physical approach for personalised smart, connected product co-development in a cloud-based environment," Journal of Intelligent Manufacturing, Springer, vol. 31(1), pages 3-18, January.
    3. Ercan Oztemel & Samet Gursev, 2020. "Literature review of Industry 4.0 and related technologies," Journal of Intelligent Manufacturing, Springer, vol. 31(1), pages 127-182, January.
    4. Mingzhou Liu & Jing Ma & Ling Lin & Maogen Ge & Qiang Wang & Conghu Liu, 2017. "Intelligent assembly system for mechanical products and key technology based on internet of things," Journal of Intelligent Manufacturing, Springer, vol. 28(2), pages 271-299, February.
    5. Marc A. Rosen & Hossam A. Kishawy, 2012. "Sustainable Manufacturing and Design: Concepts, Practices and Needs," Sustainability, MDPI, vol. 4(2), pages 1-21, January.
    6. Carla Gonçalves Machado & Mats Peter Winroth & Elias Hans Dener Ribeiro da Silva, 2020. "Sustainable manufacturing in Industry 4.0: an emerging research agenda," International Journal of Production Research, Taylor & Francis Journals, vol. 58(5), pages 1462-1484, March.
    7. Francesco Modica & Valeria Marrocco & Giacomo Copani & Irene Fassi, 2011. "Sustainable Micro-Manufacturing of Micro-Components via Micro Electrical Discharge Machining," Sustainability, MDPI, vol. 3(12), pages 1-14, December.
    8. Jian Zhang & Guofu Ding & Yisheng Zou & Shengfeng Qin & Jianlin Fu, 2019. "Review of job shop scheduling research and its new perspectives under Industry 4.0," Journal of Intelligent Manufacturing, Springer, vol. 30(4), pages 1809-1830, April.
    9. Xifan Yao & Jiajun Zhou & Yingzi Lin & Yun Li & Hongnian Yu & Ying Liu, 2019. "Smart manufacturing based on cyber-physical systems and beyond," Journal of Intelligent Manufacturing, Springer, vol. 30(8), pages 2805-2817, December.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Shuting Wang & Jie Meng & Yuanlong Xie & Liquan Jiang & Han Ding & Xinyu Shao, 2023. "Reference training system for intelligent manufacturing talent education: platform construction and curriculum development," Journal of Intelligent Manufacturing, Springer, vol. 34(3), pages 1125-1164, March.
    2. Masoud Zafarzadeh & Magnus Wiktorsson & Jannicke Baalsrud Hauge, 2021. "A Systematic Review on Technologies for Data-Driven Production Logistics: Their Role from a Holistic and Value Creation Perspective," Logistics, MDPI, vol. 5(2), pages 1-32, April.
    3. Özköse, Hakan & Güney, Gül, 2023. "The effects of industry 4.0 on productivity: A scientific mapping study," Technology in Society, Elsevier, vol. 75(C).
    4. Peerally, Jahan Ara & Santiago, Fernando & De Fuentes, Claudia & Moghavvemi, Sedigheh, 2022. "Towards a firm-level technological capability framework to endorse and actualize the Fourth Industrial Revolution in developing countries," Research Policy, Elsevier, vol. 51(10).
    5. Sihan Huang & Guoxin Wang & Shiqi Nie & Bin Wang & Yan Yan, 2023. "Part family formation method for delayed reconfigurable manufacturing system based on machine learning," Journal of Intelligent Manufacturing, Springer, vol. 34(6), pages 2849-2863, August.
    6. Elisa Negri & Vibhor Pandhare & Laura Cattaneo & Jaskaran Singh & Marco Macchi & Jay Lee, 2021. "Field-synchronized Digital Twin framework for production scheduling with uncertainty," Journal of Intelligent Manufacturing, Springer, vol. 32(4), pages 1207-1228, April.
    7. Hien Nguyen Ngoc & Ganix Lasa & Ion Iriarte, 2022. "Human-centred design in industry 4.0: case study review and opportunities for future research," Journal of Intelligent Manufacturing, Springer, vol. 33(1), pages 35-76, January.
    8. Acquah, Innocent Senyo Kwasi & Quaicoe, Judith & Gatsi, John Gartchie, 2024. "Modelling circular economy capabilities and sustainable manufacturing practices for environmental performance: Assessing linear (PLS-SEM) and non-linear (fsQCA) effects," Technological Forecasting and Social Change, Elsevier, vol. 205(C).
    9. Ali Bastas, 2021. "Sustainable Manufacturing Technologies: A Systematic Review of Latest Trends and Themes," Sustainability, MDPI, vol. 13(8), pages 1-22, April.
    10. Chenxi Yuan & Guoyan Li & Sagar Kamarthi & Xiaoning Jin & Mohsen Moghaddam, 2022. "Trends in intelligent manufacturing research: a keyword co-occurrence network based review," Journal of Intelligent Manufacturing, Springer, vol. 33(2), pages 425-439, February.
    11. Luis Fonseca & António Amaral & José Oliveira, 2021. "Quality 4.0: The EFQM 2020 Model and Industry 4.0 Relationships and Implications," Sustainability, MDPI, vol. 13(6), pages 1-20, March.
    12. Kung-Jeng Wang & Diwanda Ageng Rizqi & Hong-Phuc Nguyen, 2021. "Skill transfer support model based on deep learning," Journal of Intelligent Manufacturing, Springer, vol. 32(4), pages 1129-1146, April.
    13. Vasja Roblek & Maja Meško & Mirjana Pejić Bach & Oshane Thorpe & Polona Šprajc, 2020. "The Interaction between Internet, Sustainable Development, and Emergence of Society 5.0," Data, MDPI, vol. 5(3), pages 1-27, September.
    14. Palash Saha & Subrata Talapatra & H. M. Belal & Victoria Jackson, 2022. "Unleashing the Potential of the TQM and Industry 4.0 to Achieve Sustainability Performance in the Context of a Developing Country," Global Journal of Flexible Systems Management, Springer;Global Institute of Flexible Systems Management, vol. 23(4), pages 495-513, December.
    15. Zhang, XiaoLi & Liu, ChenGuang & Li, WenJuan & Evans, Steve & Yin, Yong, 2017. "Effects of key enabling technologies for seru production on sustainable performance," Omega, Elsevier, vol. 66(PB), pages 290-307.
    16. Tao Ren & Yan Zhang & Shuenn-Ren Cheng & Chin-Chia Wu & Meng Zhang & Bo-yu Chang & Xin-yue Wang & Peng Zhao, 2020. "Effective Heuristic Algorithms Solving the Jobshop Scheduling Problem with Release Dates," Mathematics, MDPI, vol. 8(8), pages 1-25, July.
    17. Jungmin An & Dong-Kwan Kim & Jinyeong Lee & Sung-Kwan Joo, 2021. "Least Squares Monte Carlo Simulation-Based Decision-Making Method for Photovoltaic Investment in Korea," Sustainability, MDPI, vol. 13(19), pages 1-14, September.
    18. Govindan, Kannan & Kannan, Devika & Jørgensen, Thomas Ballegård & Nielsen, Tim Straarup, 2022. "Supply Chain 4.0 performance measurement: A systematic literature review, framework development, and empirical evidence," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 164(C).
    19. Maria Polorecka & Jozef Kubas & Pavel Danihelka & Katarina Petrlova & Katarina Repkova Stofkova & Katarina Buganova, 2021. "Use of Software on Modeling Hazardous Substance Release as a Support Tool for Crisis Management," Sustainability, MDPI, vol. 13(1), pages 1-15, January.
    20. Tiago Afonso & Anabela C. Alves & Paula Carneiro, 2021. "Lean Thinking, Logistic and Ergonomics: Synergetic Triad to Prepare Shop Floor Work Systems to Face Pandemic Situations," International Journal of Global Business and Competitiveness, Springer, vol. 16(1), pages 62-76, December.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:spr:joinma:v:33:y:2022:i:8:d:10.1007_s10845-021-01782-3. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.springer.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.