A Semantic Offsite Construction Digital Twin- Offsite Manufacturing Production Workflow (OPW) OntologyDownload PDF

Mar 07, 2021 (edited Apr 23, 2021)ESWC 2021 Workshop SeDiT SubmissionReaders: Everyone
  • Keywords: Offsite Manufacturing, Production Workflow, Digital Twins, Ontologies, Process Modelling
  • Abstract: Offsite Manufacturing (OSM) is a modern and innovative method of construction with the potential to adopt advanced factory production system through a more structured workflow, standardised products, and the use of robotics for automation. However, there have been challenges in quantifying improvements from the conventional method, which leads to the low uptake. The concept of a digital twin (DT) is useful for OSM, which enables production to be represented virtually and visually including all activities associated with it, resources, and workflow involved. Thus, essential information in the product development process such as cost, time, waste, and environmental impacts can be assessed. However, the data required to have accurate results and better-informed decision-making come from heterogeneous data formats (i.e. spreadsheets and BIM models) and across different domains. The inclusion of semantic web technologies such as Linked Data (LD) and Web Ontology Language (OWL) models has proven to better address these challenges especially in terms of interoperability and unambiguous knowledge systematisation. Through an extensive systematic literature review followed up by a case study, an ontology knowledge structure representing the production workflow for OSM is developed. A real-life use case of a semi-automated production line of wall panel production is used to test and demonstrate the benefits of the semantic digital twin in obtaining cost and time data of the manufacturing for assessment. Results demonstrated the potential capability and power of capturing knowledge for an ontology to assess production workflow in terms of cost, time, carbon footprint thereby enabling more informed decision making for continuous improvements.
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