Automating the selection process of operating resources in production engineering, e.g., measurement systems, using data-driven and rule-based approaches, to improve product qualities remains challenging since it involves human experience and decentralized knowledge of different domains. A decision support system for selecting measurement systems that can be operated in various domains and industries is missing as existing approaches lack applicability in varying domains, and a centralized knowledge base is missing. This work develops an ontology for measurement systems and tasks to ensure interoperability across domains and applications. Based on this ontology, a knowledge-based decision support system implementing case-based reasoning for selecting measurement systems for a given task is developed. The decision support system recommends suitable measurement systems based on similar measurement tasks performed in the past. Analogical reasoning is implemented using knowledge graph embeddings. A centralized knowledge base, consisting of measurement systems and tasks, is instantiated using the example of battery cell manufacturing. The decision support system is then validated by drawing analogical conclusions from the example of battery and fuel cell manufacturing.
