## 5 Conclusions  
This work aims to accelerate material optimization and discovery by digitalizing data from material process chains in a connected manner, exposing the composition-process-structure-property chain in MSE. Knowledge graphs link data from various processes within material process chains. Using a generic template as the core structure for creating semantic models simplifies information retrieval, allowing for tracking the complete history of specific labeled objects. Additionally, using ontologies ensures unambiguous and interoperable data enriched with domain knowledge. A digitalization framework, the so-called Graph Designer Workflow, is presented to address these needs. It is an ontology-agnostic robust methodology for storing process-specific data and domain knowledge into linked knowledge graphs. It includes an ontology-based generic Process Graph template and step-by-step instructions for applying the methodology to create digital process chains. The resulting machine-understandable data adheres to the FAIR principles. The entire pipeline is presented in a didactic manner, making it easy for MSE-domain experts to adopt the workflows for their own digitalization requirements. Furthermore, the proposed digitalization methodology is demonstrated on a use case involving the permanent mold casting of AlSi10 Mg, encompassing variations in chemical composition, specimen extraction, heat treatment, non-destructive characterization and mechanical testing. To this end, the BWMD Ontology is used. The resulting connected knowledge graph, or knowledge base, is employed to retrieve and analyze data to investigate the influence of heat treatment and magnesium content on the strength properties characterized by tensile tests. MSE-related examples of knowledge extraction are also presented, including the retrieval of the digital process chain of a labeled object, showcasing the product traceability feature within the Graph Designer Workflow. From a scientific and technical point of view, the following increments were made concerning the digital mapping of material-intensive process chains:  
* The BWMD Ontology was introduced, which is based on the Basic Formal Ontology and provides the necessary mid-level and domain-specific vocabulary for the use case of AlSi10 Mg. The mid-level and domain-level parts of the ontology are also available as separate modules. The mid-level ontology module is designed to be general to the MSE domain. Comparisons and connections were made to parallel mid-level ontology design efforts in the MSE domain, highlighting the need to create mappings between these ontologies. * A generic Process Graph template for mapping material data along process chains was introduced, respecting the taxonomy and semantic rules of the mid-level BWMD Ontology. The Process Graph template enables a digital representation of impartible processes that are part of material process chains. While digitalization efforts like PMD [21] and Urwerk [61] have described modeling of specific processes and process chains in MSE, the present work provides a generic template that can be adapted for modeling any impartible processes. Further, the 'instantiation' of this template for ten different processes was presented, demonstrating its versatility. * The Graph Designer Workflow was presented to generate semantic RDF data. The tools part of the workflow are publicly available in [64]. An interview template is introduced to reduce the knowledge gap between domain and ontology experts, aiming to regulate and formalize information exchange. The digitalization workflow is designed to make the user interaction steps easy and intuitive. Users can design the data schema for describing processes in the interview template and later in a GUI without mastering any schema languages. Tools are created to convert the GUI template to Excel files, which are used for data annotation.