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Developing a more automated industrial digital thread is vital to realize the smart manufacturing and industry 4.0 vision. The digital thread allows for efficient sharing across product lifecycle stages. Current techniques are not robust in relating downstream data, such as manufacturing and inspection information, back to design for better decision making. We previously presented a methodology that aligns numerical control (NC) code, a standard for representing machine tool instructions, to controller data represented in MTConnect, a standard that provides a vocabulary for generalizing execution logs from different machine tools and devices. This paper extends our previous work by automating the tool identification using a k-means clustering algorithm to refine the alignment of the data. In doing so, we compare different distance techniques to analyze the spatial-temporal registration of the two datasets, i.e., the NC code and MTConnect data. Then, we assess the efficiency of our method through an error measurement technique that expresses the quality of the alignment. Finally, we apply our methodology to a case study that includes verified process plans and real execution data, derived from the smart manufacturing systems test bd hosted at the National Institute of Standards and Technology. Our analysis shows that dynamic time warping achieves the best point registration with the least errors compared with other alignment techniques.

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.