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ontostep is a method to translate the STandard for the Exchange of Product Model Data (STEP) schema and its instances to an ontology and knowledge graphs represented in the web ontology language (owl). ontostep models can be integrated with any owl models to enrich their semantics. However, the current implementation has several limitations, mainly in (1) supporting the latest International Organization for Standardization (ISO) 10303 schemas and (2) generating various representation types depending on the purpose of use. We present an improved implementation of ontostep to overcome these limitations. In this article, we demonstrate that the new implementation can successfully translate STEP schemas and instances in a faster and more flexible way, thus furthering the adoption of the full capabilities of ISO 10303. By encoding STEP entities in owl, we facilitate integration with other standards through knowledge graphs.

Realizing the digital thread is essential for linking and orchestrating data across the product lifecycle in smart manufacturing. Linking heterogeneous lifecycle data is critical to maintain associativity and traceability in a digital thread. Recently, researchers have successfully leveraged ontology models with knowledge graphs in engineering domains for threading different lifecycle data. One of the most successful of such efforts is OntoSTEP which enables the formal capture of information embedded in the STandard for Exchange of Product model data (STEP) data representation, or ISO 10303. Meanwhile, an emerging inspection standard, called the Quality Information Framework (QIF), has garnered significant attention as it can bring quality information into the digital thread. Implementing more automated methods for product quality assurance is challenging due to the lack of unified information models from design to inspection. To this end, we propose an approach to fuse as-designed data represented in STEP and as-inspected data represented in QIF in a standards-based digital thread based on ontology with knowledge graphs. Specifically, we present an automated pipeline for generating knowledge graphs representing STEP and QIF data, a mapping implementation to integrate STEP and QIF knowledge graphs, and rules and queries to demonstrate the integration’s potential for better decision making with respect to product quality assurance.