Semantic modelling of Process-Structure-Property dependencies of rolled metal sheets

Unraveling process-structure-property (PSP) dependencies in materials science and engineering remains a significant challenge due to the highly complex interdependencies, heterogeneity and incompatibility of data across various sources. Ontology-based knowledge representation offers a framework for the formal, machine-actionable description of such dependencies, supporting FAIR data principles and logical reasoning. PSP dependencies are particularly complex for rolled metal sheets, whose manufacturing process chain determines the resulting microstructure and crystallographic texture. The latter governs the anisotropy of functional properties, which is critical for material selection and performance optimization. In this work, semantic models are presented for describing PSP dependencies in rolled metal sheets, covering the manufacturing process chain, microstructural features, crystallographic texture, and the anisotropy of functional properties. The models are aligned with the Basic Formal Ontology (BFO) via the Platform MaterialDigital Core Ontology (PMDco 3.0), ensuring semantic interoperability. The framework is validated for the use case of non-oriented electrical steels (NOES), an essential material for electromobility, to demonstrate the applicability of the proposed models to an industrial domain. The presented semantic models enable systematic data integration and knowledge reuse, and provide a foundation for graph-based, data driven approaches for multi-objective materials optimization.