IDEAS home Printed from https://ideas.repec.org/a/gam/jftint/v10y2018i8p70-d160664.html
   My bibliography  Save this article

Multidiscipline Integrated Platform Based on Probabilistic Analysis for Manufacturing Engineering Processes

Author

Listed:
  • Lijun Zhang

    (National Center for Materials Service Safety, University of Science and Technology Beijing, Beijing 100083, China)

  • Kai Liu

    (National Center for Materials Service Safety, University of Science and Technology Beijing, Beijing 100083, China)

  • Jian Liu

    (School of Civil and Resource Engineering, University of Science and Technology Beijing, Beijing 100083, China)

Abstract

Researchers from different disciplines, such as materials science, computer science, safety science, mechanical engineering and controlling engineering, have aimed to improve the quality of manufacturing engineering processes. Considering the requirements of research and development of advanced materials, reliable manufacturing and collaborative innovation, a multidiscipline integrated platform framework based on probabilistic analysis for manufacturing engineering processes is proposed. The proposed platform consists of three logical layers: The requirement layer, the database layer and the application layer. The platform is intended to be a scalable system to gradually supplement related data, models and approaches. The main key technologies of the platform, encapsulation methods, information fusion approaches and the collaborative mechanism are also discussed. The proposed platform will also be gradually improved in the future. In order to exchange information for manufacturing engineering processes, scientists and engineers of different institutes of materials science and manufacturing engineering should strengthen their cooperation.

Suggested Citation

  • Lijun Zhang & Kai Liu & Jian Liu, 2018. "Multidiscipline Integrated Platform Based on Probabilistic Analysis for Manufacturing Engineering Processes," Future Internet, MDPI, vol. 10(8), pages 1-10, July.
    • Handle: RePEc:gam:jftint:v:10:y:2018:i:8:p:70-:d:160664
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1999-5903/10/8/70/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/1999-5903/10/8/70/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Pierre-Julien Trombe & Pierre Pinson & Henrik Madsen, 2012. "A General Probabilistic Forecasting Framework for Offshore Wind Power Fluctuations," Energies, MDPI, vol. 5(3), pages 1-37, March.
    2. Virginia Pilloni, 2018. "How Data Will Transform Industrial Processes: Crowdsensing, Crowdsourcing and Big Data as Pillars of Industry 4.0," Future Internet, MDPI, vol. 10(3), pages 1-14, March.
    3. Zoubin Ghahramani, 2015. "Probabilistic machine learning and artificial intelligence," Nature, Nature, vol. 521(7553), pages 452-459, May.
    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. Masafumi Nakano & Akihiko Takahashi & Soichiro Takahashi, 2018. "State Space Approach to Adaptive Artificial Intelligence Modeling: Application to Financial Portfolio with Fuzzy System," CARF F-Series CARF-F-422, Center for Advanced Research in Finance, Faculty of Economics, The University of Tokyo.
    2. Wilson, Christopher & van der Velden, Maja, 2022. "Sustainable AI: An integrated model to guide public sector decision-making," Technology in Society, Elsevier, vol. 68(C).
    3. repec:ags:aaea22:335590 is not listed on IDEAS
    4. Mihui Kim & Junhyeok Yun, 2020. "Development of User-Participatory Crowdsensing System for Improved Privacy Preservation," Future Internet, MDPI, vol. 12(3), pages 1-19, March.
    5. Li, Yi & Liu, Kailong & Foley, Aoife M. & Zülke, Alana & Berecibar, Maitane & Nanini-Maury, Elise & Van Mierlo, Joeri & Hoster, Harry E., 2019. "Data-driven health estimation and lifetime prediction of lithium-ion batteries: A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 113(C), pages 1-1.
    6. Kim, Sung Wook & Oh, Ki-Yong & Lee, Seungchul, 2022. "Novel informed deep learning-based prognostics framework for on-board health monitoring of lithium-ion batteries," Applied Energy, Elsevier, vol. 315(C).
    7. Xuequn Wang & Xiaolin Lin & Bin Shao, 2023. "Artificial intelligence changes the way we work: A close look at innovating with chatbots," Journal of the Association for Information Science & Technology, Association for Information Science & Technology, vol. 74(3), pages 339-353, March.
    8. Himanshu Sharma & Anu G. Aggarwal, 2022. "Segmenting Reviewers Based on Reviewer and Review Characteristics," International Journal of Business Analytics (IJBAN), IGI Global, vol. 9(1), pages 1-20, January.
    9. Fekadu Agmas Wassie & László Péter Lakatos, 2024. "Artificial intelligence and the future of the internal audit function," Palgrave Communications, Palgrave Macmillan, vol. 11(1), pages 1-13, December.
    10. Qiqin Zhou, 2024. "Explainable AI in Request-for-Quote," Papers 2407.15038, arXiv.org.
    11. Joaquim AP Braga & António R Andrade, 2019. "Optimizing maintenance decisions in railway wheelsets: A Markov decision process approach," Journal of Risk and Reliability, , vol. 233(2), pages 285-300, April.
    12. repec:hum:wpaper:sfb649dp2015-026 is not listed on IDEAS
    13. Manis, K.T. & Madhavaram, Sreedhar, 2023. "AI-Enabled marketing capabilities and the hierarchy of capabilities: Conceptualization, proposition development, and research avenues," Journal of Business Research, Elsevier, vol. 157(C).
    14. Luigi Spezia & Andy Vinten & Roberta Paroli & Marc Stutter, 2021. "An evolutionary Monte Carlo method for the analysis of turbidity high‐frequency time series through Markov switching autoregressive models," Environmetrics, John Wiley & Sons, Ltd., vol. 32(8), December.
    15. Changhao Zhang & Mengyu Ren, 2023. "Customer service robot model based on e-commerce dual-channel channel supply coordination and compensation strategy in the perspective of big data," International Journal of System Assurance Engineering and Management, Springer;The Society for Reliability, Engineering Quality and Operations Management (SREQOM),India, and Division of Operation and Maintenance, Lulea University of Technology, Sweden, vol. 14(2), pages 591-601, April.
    16. Chen, Xia & Geyer, Philipp, 2022. "Machine assistance in energy-efficient building design: A predictive framework toward dynamic interaction with human decision-making under uncertainty," Applied Energy, Elsevier, vol. 307(C).
    17. Moll, Jodie & Yigitbasioglu, Ogan, 2019. "The role of internet-related technologies in shaping the work of accountants: New directions for accounting research," The British Accounting Review, Elsevier, vol. 51(6).
    18. Martin Obschonka & David B. Audretsch, 2020. "Artificial intelligence and big data in entrepreneurship: a new era has begun," Small Business Economics, Springer, vol. 55(3), pages 529-539, October.
    19. Damianos P. Sakas & Nikolaos Th. Giannakopoulos, 2021. "Harvesting Crowdsourcing Platforms’ Traffic in Favour of Air Forwarders’ Brand Name and Sustainability," Sustainability, MDPI, vol. 13(15), pages 1-25, July.
    20. Mathieu Pichault & Claire Vincent & Grant Skidmore & Jason Monty, 2021. "Short-Term Wind Power Forecasting at the Wind Farm Scale Using Long-Range Doppler LiDAR," Energies, MDPI, vol. 14(9), pages 1-21, May.
    21. He, Wenbin & Liu, Ting & Ming, Wuyi & Li, Zongze & Du, Jinguang & Li, Xiaoke & Guo, Xudong & Sun, Peiyan, 2024. "Progress in prediction of remaining useful life of hydrogen fuel cells based on deep learning," Renewable and Sustainable Energy Reviews, Elsevier, vol. 192(C).
    22. Julian Senoner & Bernhard Kratzwald & Milan Kuzmanovic & Torbjørn H. Netland & Stefan Feuerriegel, 2023. "Addressing distributional shifts in operations management: The case of order fulfillment in customized production," Production and Operations Management, Production and Operations Management Society, vol. 32(10), pages 3022-3042, October.

    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:gam:jftint:v:10:y:2018:i:8:p:70-:d:160664. 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: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.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.