## 4 Discussion
The proposed methodology is demonstrated across ten different processes (see Table 1), showcasing the generality of the solution and the semantic depth of the BWMD Ontology. As the disparity of modeled process data increases, more concepts are required to encapsulate the knowledge, leading to an enriched ontology vocabulary. In addition to the RDF graph, two Jupyter Notebooks are also accessible with the created knowledge base for the cast ASi10 Mg [33], including further SPARQL queries and data visualization features. This aims to add a dimension of user experience to the presented study, as this article intends to be informative and didactic. The advantages of the generic Process Graph template become apparent through the introduced SPARQL query examples in Section 3.2. By following the core pattern of the generic process data model, generic query algorithms can be developed to efficiently and easily extract information stored in the RDF graph connecting all the different digitalizedprocesses. The structure enables retrieval of specific material properties generated via one specific test (see Section 3.2.1) and allows the extraction of other types of information, such as the controlled parameters applied to conduct one specific test (see Section 3.2.2). Unlike traditional SQL databases, graph databases allow for extracting both tabular and graph data based on patterns, thanks to the CONSTRUCT SPARQL query structure. This is showcased in Section 3.2.3, where the digital process chain of one specific object is accessed via SPARQL query. The retrieved object is a piece of the complete RDF Graph and can be visualized as displayed in Figure 13. This example shows the utility of the proposed methodology in creating a data infrastructure that enables a product traceability feature along its digitalized manufacturing process chain. Both the generic Process Graph template and the 'process chain' query algorithm could be extended to include further process-related information details such as the \(CO_{2}\) footprint or the cost associated with the conducted manufacturing step, which could later be used in conjunction with other object history information to find production routes for decreased environmental impact or for reducing production costs of a particular product. Access to product history information enables the discovery of new production patterns, leading to optimized material properties, as illustrated in Section 3.3. Feeding AI models with context-rich data enhances the material design process, helping to find optimal production paths and allowing for quick solutions to describe phenomena not considered in physical models. If an AI-trained model is fed with a high-quality database (as would be the case of a context-rich graph database), reliable predictions can be made. In the MSE field, this could imply significant cost reduction by creating reduced or optimized material testing programs. Another advantage would be developing robust hybrid models, which would better reproduce the dispersion of apparently similarly generated material experimental data, i.e., generated from similar testing conditions. In such cases, the material scatter can be explained by small differences detected in the control parameters along the digitalized process chain of each tested specimen, e.g., in one or several heat treatment steps, as showcased for the material AlSi10 Mg regarding the impact of aging duration on the resulting tensile strength of the material. While this information is common knowledge for the material production expert, it is not necessarily shared by the material engineer who performs simulations with this material, and the information might remain hidden in the form of experimental scatter. Another hidden effect discussed in Section 3.3 is the impact of the variation of the Mg amount in the melt on the tensile strength of the investigated material. The presented Graph Designer Workflow and the BWMD Ontology provide a robust framework for digitalizing material data and creating digital process chains.