Abstract
This paper introduces the Steel Cold Rolling Ontology (SCRO) to model and capture domain knowledge of cold rolling processes and activities within a steel plant. A case study is set up that uses real-world cold rolling data sets to validate the performance and functionality of SCRO. This includes using the Ontop framework to deploy virtual knowledge graphs for data access, data integration, data querying, and condition-based maintenance purposes. SCRO is evaluated using OOPS!, the ontology pitfall detection system, and feedback from domain experts from Tata Steel.
Highlights
The goal of this study is to develop an ontology that focuses on modeling the cold rolling processes that occur during steelmaking
This paper presents a novel steel cold rolling ontology that models and structures domain knowledge of cold rolling processes and activities within a steel plant
The purpose of the ontology is to improve data semantics and interoperability within the domain of smart manufacturing, which is the first step towards achieving Industry 4.0
Summary
In the domain of smart manufacturing, ontologies can play a key role as they are able to provide machine-understandable vocabularies and data exchange between different individuals and processes. The goal of this study is to develop an ontology that focuses on modeling the cold rolling processes that occur during steelmaking. Cold Rolling Ontology (SCRO) that acts as a knowledge base for cold rolling processes within a steel manufacturing plant. This includes the relevant systems, facilities, hardware, software, and inventory of a cold rolling mill. To validate and evaluate the usefulness and accuracy of SCRO, we perform a case study that aligns the ontology with real-world data sets of a cold rolling mill provided by Tata Steel (https://www.tatasteeleurope.com/ts/, accessed on 26 July 2021).
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