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Analysis and optimization based on reusable knowledge base of process performance models

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Abstract

In this paper, we propose an architectural design and software framework for fast development of descriptive, diagnostic, predictive, and prescriptive analytics solutions for dynamic production processes. The proposed architecture and framework will support the storage of modular, extensible, and reusable knowledge base (KB) of process performance models. The approach requires developing automated methods that can translate the high-level models in the reusable KB into low-level specialized models required by a variety of underlying analysis tools, including data manipulation, optimization, statistical learning, estimation, and simulation. We also propose an organization and key structure for the reusable KB, composed of atomic and composite process performance models and domain-specific dashboards. Furthermore, we illustrate the use of the proposed architecture and framework by prototyping a decision support system for process engineers. The decision support system allows users to hierarchically compose and optimize dynamic production processes via a graphical user interface.

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Authors and Affiliations

  1. Department of Computer Science, George Mason University, Fairfax, VA, USA

    Alexander Brodsky, Mohan Krishnamoorthy & Daniel Menascé

  2. Engineering Laboratory, National Institute of Standards and Technology (NIST), Gaithersburg, MD, USA

    Guodong Shao & Ronay Ak

  3. Department of Mechanical Engineering, University of Maryland, College Park, MD, USA

    Anantha Narayanan

Authors
  1. Alexander Brodsky
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  2. Guodong Shao
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  3. Mohan Krishnamoorthy
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  4. Anantha Narayanan
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  5. Daniel Menascé
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  6. Ronay Ak
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Corresponding author

Correspondence to Guodong Shao.

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No approval or endorsement of any commercial product by the National Institute of Standards and Technology is intended or implied. Certain commercial software systems are identified in this paper to facilitate understanding. Such identification does not imply that these software systems are necessarily the best available for the purpose.

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Brodsky, A., Shao, G., Krishnamoorthy, M. et al. Analysis and optimization based on reusable knowledge base of process performance models. Int J Adv Manuf Technol 88, 337–357 (2017). https://doi.org/10.1007/s00170-016-8761-7

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