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Analysis and Optimization based on Reusable Knowledge Base of Process Performance Models.

Alexander Brodsky1, Guodong Shao2, Mohan Krishnamoorthy1

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

The International Journal, Advanced Manufacturing Technology
|July 6, 2019
PubMed
Summary
This summary is machine-generated.

This paper introduces a new framework for developing analytics solutions for dynamic production processes. It enables fast creation of descriptive, diagnostic, predictive, and prescriptive models using a reusable knowledge base.

Keywords:
Smart manufacturingdata analyticsdomain specific user interfaceoptimizationprocess performance modelsreusable knowledge base

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Area of Science:

  • Industrial Engineering
  • Data Science
  • Software Engineering

Background:

  • Dynamic production processes require advanced analytics for optimization.
  • Existing solutions for process analytics are often slow to develop and lack reusability.
  • A modular and extensible framework is needed for efficient development of diverse analytics.

Purpose of the Study:

  • To propose an architectural design and software framework for rapid development of analytics solutions.
  • To support a Knowledge Base (KB) of modular, extensible, and reusable process performance models.
  • To facilitate the translation of high-level models into low-level specialized models for various analysis tools.

Main Methods:

  • Developed an architecture and framework supporting a reusable Knowledge Base (KB).
  • Proposed methods for automated translation of high-level KB models to low-level analysis models.
  • Designed an organization for the KB, including atomic/composite models and dashboards.
  • Prototyped a decision-support system for process engineers.

Main Results:

  • The framework supports modular, extensible, and reusable process performance models.
  • Automated translation methods enable integration with data manipulation, optimization, statistical learning, estimation, and simulation tools.
  • A prototype decision-support system demonstrates hierarchical composition and optimization of production processes.
  • The system features a user-friendly graphical interface for process engineers.

Conclusions:

  • The proposed architecture and framework enable fast development of comprehensive analytics solutions for dynamic production processes.
  • The reusable Knowledge Base and automated translation methods enhance efficiency and model adaptability.
  • The decision-support system empowers process engineers to effectively compose and optimize production processes.