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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
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    References listed on IDEAS

    as
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    2. Zoubin Ghahramani, 2015. "Probabilistic machine learning and artificial intelligence," Nature, Nature, vol. 521(7553), pages 452-459, May.
    3. 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.
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