This study applies Semantic Web technologies to advance Materials Science and Engineering (MSE) through the integration of diverse datasets. Focusing on a 2000 series age-hardenable aluminum alloy, we correlate mechanical and microstructural properties derived from tensile tests and dark-field transmission electron microscopy across varied aging times. An expandable knowledge graph, constructed using the Tensile Test and Precipitate Geometry Ontologies aligned with the PMD Core Ontology, facilitates this integration. This approach adheres to FAIR principles and enables sophisticated analysis via SPARQL queries, revealing correlations consistent with the Orowan mechanism. The study highlights the potential of semantic data integration in MSE, offering a new approach for data-centric research and enhanced analytical capabilities.

本研究应用语义网技术通过整合不同的数据集来推进材料科学与工程 (MSE)。我们以 2000 系列时效硬化铝合金为重点，将不同时效时间内拉伸试验和暗场透射电子显微镜得出的机械和微观结构性能关联起来。使用与 PMD 核心本体一致的拉伸测试和沉淀几何本体构建的可扩展知识图促进了这种集成。这种方法遵循 FAIR 原则，并通过 SPARQL 查询实现复杂的分析，揭示与 Orowan 机制一致的相关性。该研究强调了 MSE 中语义数据集成的潜力，为以数据为中心的研究和增强的分析能力提供了一种新方法。