A knowledge-enriched computational model to support lifecycle activities of computational models in smart manufacturing

Heng Zhang, Utpal Roy

Research output: Contribution to journalArticlepeer-review

1 Scopus citations

Abstract

Because of the needs in developing industrial community modeling and simulation platforms for smart manufacturing, a Knowledge-Enriched Computational Model (KECM) is proposed in this article to formally capture domain knowledge and integrate that knowledge with standardized computational models. To support model development in a distributed environment, the KECM can be used as the medium for formal information sharing between model developers. A case study has been developed to demonstrate the utilization of the KECM in supporting the construction of a Bayesian Network model between a domain expert and data analyst. To support model deployment, a general method to support the integration of computational models into a manufacturing data system has been presented. A case study has been developed to show the data integration of an optimization model into a Business To Manufacturing Markup Language-based manufacturing system. Finally, a general model to support the combination of computational models have been presented. A case study has been developed to show the combination of an Agent-based model and a Decision Tree model using the KECM.

Original languageEnglish (US)
Pages (from-to)227-249
Number of pages23
JournalSmart and Sustainable Manufacturing Systems
Volume2
Issue number1
DOIs
StatePublished - Nov 30 2018

Keywords

  • Domain knowledge
  • Model combination
  • Model deployment
  • Model development
  • Smart manufacturing
  • Standardized computational model

ASJC Scopus subject areas

  • Control and Systems Engineering
  • Industrial and Manufacturing Engineering
  • Computer Science Applications

Fingerprint

Dive into the research topics of 'A knowledge-enriched computational model to support lifecycle activities of computational models in smart manufacturing'. Together they form a unique fingerprint.

Cite this