Ontology-based uncertainty management approach in designing of robust decision workflows

Abstract

The formal representation and capturing of uncertainty knowledge are always essential but difficult. Typically, the uncertainties due to incompleteness and inaccuracies of model information in engineering design necessitate the designing of robust decision workflows to improve the quality of process/product in variations. This requests extending a designer's abilities in managing various uncertainties in system design and making decisions that are robust, flexible, and comprehensive. To enable the management of various uncertainties, in this paper, we propose an ontology for robust design and a template-based ontological method that is employed to design decision workflows. We achieve the aforementioned goals through the identification of: (1) procedural knowledge – defining a procedure of designing robust decision workflows, including the sequence of activities, to determine the right combination of design information for a specific type of uncertainty, and (2) declarative knowledge – developing a frame-based ontology for the formal representation of tacit knowledge to capture and document the re-usable information of a robust design by utilising the process templates. We demonstrate the efficacy by carrying out the robust design of the hot rod rolling process based on the analysis and synthesis of the processing-microstructure (cooling module) and the microstructure-mechanical (rod module) simulation models.

KEYWORDS:

• Ontology
• uncertainty management
• robust design
• decision workflows
• capture and reuse

Acknowledgments

Ru Wang gratefully acknowledges the Project funded by China Postdoctoral Science Foundation [grant 2018M640073]. Guoxin Wang and Yan Yan are very grateful to the financial support from the National Ministries Projects of China [grant JCKY2016602B007]. Janet K. Allen and Farrokh Mistree gratefully acknowledge the John and Mary Moore Chair and the L.A. Comp Chair at the University of Oklahoma. This paper is an outcome of the International Systems Realization Partnership between the Institute for Industrial Engineering @ The Beijing Institute of Technology, The Systems Realization Laboratory @ The University of Oklahoma and the Design Engineering Laboratory @ Purdue.

Disclosure statement

No potential conflict of interest was reported by the authors.

Notes

1 Graph Widget of Protégé, Stanford University, http://protegewiki.stanford.edu/wiki/Graph_Widget_Tutorial_OWL, Accessed on 1 February 2016.

2 https://protege.stanford.edu/.

Additional information

Funding

This work was supported by the National Ministries Projects of China [grant number JCKY2016602B007]; China Postdoctoral Science Foundation [grant number 2018M640073].