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

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• 作者：Alexander Brodsky ; Guodong Shao…
• 关键词：Smart manufacturing ; Data analytics ; Domain specific user interface ; Optimization ; Reusable knowledge base ; Process performance models
• 刊名：The International Journal of Advanced Manufacturing Technology
• 出版年：2017
• 出版时间：January 2017
• 年：2017
• 卷：88
• 期：1-4
• 页码：337-357
• 全文大小：
• 刊物类别：Engineering
• 刊物主题：Industrial and Production Engineering; Media Management; Mechanical Engineering; Computer-Aided Engineering (CAD, CAE) and Design;
• 出版者：Springer London
• ISSN：1433-3015
• 卷排序：88

文摘

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.