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Developing a big data analytics platform for manufacturing systems: architecture, method, and implementation

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Abstract

Manufacturing industries have recently promoted smart manufacturing (SM) for achieving intelligence, connectedness, and responsiveness of manufacturing objects consisting of man, machine, and material. Traditional manufacturing platforms, which identify generic frameworks where common functionalities are shareable and diverse applications are workable, mainly focused on remote collaboration, distributed control, and data integration; however, they are limited to incorporating those characteristic achievements. The present work introduces an SM-toward manufacturing platform. The proposed platform incorporates the capabilities of (1) virtualization of manufacturing objects for their autonomy and cooperation, (2) processing of real and various manufacturing data for mediating physical and virtual objects, and (3) data-driven decision-making for predictive planning on those objects. For such capabilities, the proposed platform advances the framework of Holonic Manufacturing Systems with the use of agent technology. It integrates a distributed data warehouse to encompass data specification, storage, processing, and retrieval. It applies a data analytics approach to create empirical decision-making models based on real and historical data. Furthermore, it uses open and standardized data interfaces to embody interoperable data exchange across shop floors and manufacturing applications. We present the architecture and technical methods for implementing the proposed platform. We also present a prototype implementation to demonstrate the feasibility and effectiveness of the platform in energy-efficient machining.

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Funding

This work was supported by the National Research Foundation of Korea (NRF) grant funded by the Korea government (MSIP) (NRF-2018R1D1A1B07047100).

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Authors and Affiliations

  1. Information Technology Laboratory, National Institute of Standards and Technology, 100 Bureau Drive, Gaithersburg, MD, 20899, USA

    Jungyub Woo

  2. Graduate School of Management of Technology, Pukyong National University, Engineering Building No. 3, 365 Shinseon-ro, Nam-gu, Busan, 48547, Republic of Korea

    Seung-Jun Shin, Wonchul Seo & Prita Meilanitasari

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  1. Jungyub Woo
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  2. Seung-Jun Shin
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  4. Prita Meilanitasari
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Correspondence to Seung-Jun Shin.

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Woo, J., Shin, SJ., Seo, W. et al. Developing a big data analytics platform for manufacturing systems: architecture, method, and implementation. Int J Adv Manuf Technol 99, 2193–2217 (2018). https://doi.org/10.1007/s00170-018-2416-9

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  • DOI: https://doi.org/10.1007/s00170-018-2416-9

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