Leveraging Ontology Development to Enhance Corrosion Visualisation in Engineering Design

Designing automated solutions for understanding, visualising, and detecting damage has become a key area in the era of digitalisation and advanced engineering. The use of deep learning and other artificial intelligence approaches has proven very innovative and rendered manual methods of damage inspection primitive. However, there remains a critical gap in creating a unified knowledge base that helps in understanding, conceptualising, and promoting collaborative engineering design, particularly in the context of automated corrosion detection through images. This research addresses this gap by presenting a conceptualised model for understanding corrosion detection through ontology development. This is implemented in an ontology development environment using Protege 5.5.0 and ELK 0.5.0 reasoner. Evaluation is done using expert competency questions and hypothetical scenarios thereby establishing a robust framework that is beneficial to Engineering design in terms of terminology standardisation, design process facilitation, and building corrosion inspection systems that are interoperable. The Ontology also enables sharing and reusing knowledge between automated corrosion detection systems as well as integration with existing industrial standards such as Industrial Ontology Foundry (IOF) and semantic web standards. Hence, offering a significant contribution to the digitalisation of engineering design. Besides the enhancement of damage detection, this work also advances the engineering field to utilise visual data more effectively in design, maintenance, and product lifecycle management.