Ontological modelling of creep void analysis data to automate machine learning training process

This work focuses on the ontological representation of creep void analysis data to automate the training of the machine learning (ML) model detecting creep voids in scanning electron microscope images. Metallic high-temperature structures are subject to creep phenomenon that can lead to rupture and component failure when prolonged. ML models can be deployed to detect and obtain information about the density and location of creep voids using images as input data. However, due to the irregularities in the size and shape of creep voids and the associated uncertainty in ML models, the material engineer is required to inspect the detections and provide feedback on a regular basis. To automate the retraining of the ML model and to facilitate the close collaboration between the ML experts and the material engineers, domain ontologies providing common vocabularies can be utilized. We aligned the relevant concepts of the EMMO ontology, ML-Schema, and the PROV ontology to document creep void data for smooth information sharing and improving the quality of the ML detection process, hence resulting in better analysis and higher productivity compared to manual characterization. The SPARQL queries are used to gain valuable insights, such as the number and area of creep voids, condition of the metallic structure, and accuracy of the ML predictions.