Enriching Standards-Based Digital Thread by Fusing As-Designed and As-Inspected Data Using Ontologies

Published

August 13, 2020

Author(s)

Soonjo Kwon, William Z. Bernstein, Laetitia V. Monnier, Raphael Barbau

Abstract

Realizing the digital thread is essential for linking and orchestrating data across the product lifecycle in smart manufacturing. Linking heterogeneous lifecycle data is critical to maintain associativity and traceability in a digital thread. Recently, researchers have successfully leveraged ontology models in engineering domains for threading different lifecycle data. One of the most successful of such efforts is OntoSTEP which enables the formal capture of information embedded in the STandard for Exchange of Product data (STEP) data representation, or ISO 10303. Meanwhile, an emerging inspection standard, called the Quality Information Framework (QIF), has garnered significant attention as it can bring quality information into the digital thread. Implementing more automated methods for product quality assurance is challenging due to the lack of unified information models from design to inspection. To this end, we propose an ontology-based approach to fuse as-designed data represented in STEP and as-inspected data represented in QIF in a standards-based digital thread. Specifically, we present a workflow for generating instance ontologies representing STEP and QIF data, a method to integrate STEP and QIF instance ontologies, and rules and queries to demonstrate the integration's potential for better decision making with respect to product quality assurance.

Citation

Advanced Engineering Informatics

Volume

46

Pub Type

Journals

Download Paper

https://doi.org/10.1016/j.aei.2020.101102

Local Download

Keywords

Digital thread, Ontology, Industrial data standard, Product lifecycle, Data fusion, Integration

Citation

Kwon, S. , Bernstein, W. , Monnier, L. and Barbau, R. (2020), Enriching Standards-Based Digital Thread by Fusing As-Designed and As-Inspected Data Using Ontologies, Advanced Engineering Informatics, [online], https://doi.org/10.1016/j.aei.2020.101102 (Accessed November 20, 2024)