A Novel Digitalization Approach for Smart Materials - Ontology-Based Access to Data and Models

Smart materials react to physical fields (e.g., electric, magnetic, and thermal fields) and can be used as sensors, actuators, and generators due to their bidirectional behavior. Easy and multiscale access to material data and models enables efficient research and development with regard to the selection of appropriate materials and their optimization towards specific applications. However, different working principles, measurement and analysis methods, as well as data storage approaches lead to heterogeneous and partly inconsistent datasets. The ontology-based data access (OBDA) is a suitable method to access such heterogeneous datasets easily and quickly, while material models can transform material data across certain scales for different applications. In order to connect both capabilities, an extended approach enabling an ontology-based data and model access (OBDMA) is presented, also supporting findable, accessible, interoperable, and re-usable (FAIR). The OBDMA system comprises four main levels, the query, the ontology, the mapping, and the database. Storing knowledge at these different levels increases the interchangeability and enables variable datasets, which is essential, especially for dynamic research fields such as smart materials. In this article, the principles and advantages of the OBDMA approach are demonstrated for different subclasses of smart materials, but can be transferred to other materials, too. In order to access heterogeneous material data and model-based knowledge, the established ontology-based data access (OBDA) is extended to include material models. This novel ontology-based data and model access (OBDMA) enables the computation of new responses beyond stored data. Demonstrated across various smart material subclasses, its versatility suggests broader application possibilities.image (c) 2024 WILEY-VCH GmbH