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

Published

Author(s)

Alexander Brodsky, Guodong Shao, Mohan Krishnamoorthy, Anantha Narayanan Narayanan, Daniel Menasce?, Ronay Ak

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.
Citation
International Journal of Advanced Manufacturing Technology

Keywords

Smart manufacturing, data analytics, domain specific user interface, optimization, reusable knowledge base, process performance models

Citation

Brodsky, A. , Shao, G. , Krishnamoorthy, M. , Narayanan, A. , Menasce?, D. and Ak, R. (2016), Analysis and Optimization based on Reusable Knowledge Base of Process Performance Models, International Journal of Advanced Manufacturing Technology, [online], https://doi.org/10.1007/s00170-016-8761-7 (Accessed July 19, 2024)

Issues

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Created April 28, 2016, Updated October 12, 2021