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Construction and application of barrel finishing underlying database platform

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Abstract

The present methods of data preservation and representation for barrel finishing processes which include paper and electronic documents have several disadvantages such as restrictions in size and complexity, and limitations on query and updation speed. Aiming at these disadvantages, a new database platform for barrel finishing data has been constructed by using database technology and case-based reasoning. The design procedure of the database platform is expounded in detail, covering analysis of database platform requirements, establishment for conceptual model of database data structure, designs for logical model of database data structure, determination for physical model of database data structure, choice for network structure of database platform, data management and storage method. The application results demonstrate that the database platform can ensure the safe and convenient storage as well as the sharing of experimental data of the barrel finishing process. It can also provide guidance and technical information for scientific researchers, experts, technicians, and production site operators to choose the processing technology and the processing parameters reasonably.

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Acknowledgements

The authors acknowledge the Shanxi Scholarship Council of China (Grant: 2017-032), the National Natural Science Foundation of China (Grant: U1510118).

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Authors and Affiliations

  1. College of Mechanical Engineering, Taiyuan University of Technology, Taiyuan, China

    Wei Gao & Shengqiang Yang

  2. College of Electrical and Power Engineering, Taiyuan University of Technology, Taiyuan, China

    Jianyan Tian & Fei Yan

  3. Mechanical Engineering, Pennsylvania State University Harrisburg, Middletown, USA

    Amit Banerjee

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  1. Wei Gao
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  2. Shengqiang Yang
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  4. Amit Banerjee
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  5. Fei Yan
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Correspondence to Jianyan Tian.

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Gao, W., Yang, S., Tian, J. et al. Construction and application of barrel finishing underlying database platform. Cluster Comput 22 (Suppl 6), 15367–15378 (2019). https://doi.org/10.1007/s10586-018-2600-6

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