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Analysis and Optimization in Smart Manufacturing based on a Reusable Knowledge Base for 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, predictive, diagnostic, and prescriptive analytics solutions for dynamic production processes. The proposed architecture and framework are based on a reusable, modular, and extensible Knowledge Base (KB) of process performance models. The approach requires solving the technical challenge of automatic translation methods from a high-level uniform representation of performance models in the Reusable KB into low-level specialized models required by each of the underlying tools, including data manipulation, optimization, statistical learning, estimation, and simulation. We also propose organization and key structure of the reusable KB, composed of atomic and composite process performance models and domain-specific dashboards. Furthermore, we illustrate the use of the proposed design and framework by performing diagnostic tasks on a composite performance model.
Proceedings Title
Proceedings of the IEEE International Conference on Big Data (IEEE BigData 2015)
Conference Dates
October 29-November 1, 2015
Conference Location
Santa Clara, CA, US
Conference Title
2015 IEEE International Conference on Big Data (IEEE Big Data 2015)

Keywords

smart manufacturing, data analytics, optimization, reusable knowledge base, process performance models

Citation

Brodsky, A. , Shao, G. , Krishnamoorthy, M. , Narayanan, A. , Menasce?, D. and Ak, R. (2015), Analysis and Optimization in Smart Manufacturing based on a Reusable Knowledge Base for Process Performance Models, Proceedings of the IEEE International Conference on Big Data (IEEE BigData 2015), Santa Clara, CA, US (Accessed April 20, 2024)
Created October 31, 2015, Updated October 12, 2021