Additive Manufacturing (AM) of metallic components enables fast and near-net-shape production based on design data, and is increasingly being used in industrial series production. To understand the relationships between raw materials, process parameters, and the resulting component properties at both microscopic and macroscopic levels, test programs are being conducted worldwide. In order to structure the collection of this data and make it accessible to a wider audience, a data model that can be read by both humans and machines is required to ensure a structured exchange of data and to fundamentally capture the process, material, and component knowledge and behavior.
In this presentation, an ontology-based material data management (OBDM) approach specifically for Laser Powder Bed Fusion of metals (PBF-LB/M) using Python is outlined. The goal is to support the integration of material and process data, as well as decision-making for future component developments. The presented approach enables automatic import, formalization of material data, and raw data backup, facilitating information exchange between stakeholders and researchers in the PBF ecosystem.
The aim is to demonstrate how expert knowledge from the domains is collected through interviews to capture the nomenclature for process parameters along the process chain that can potentially influence component quality. Based on this expert knowledge, a modular ontology structure is developed, which builds on the existing Basic Formal Ontology (BFO) and Common Core Ontologies (CCO). The coherence of the model is ensured through a subject glossary and forms the basis for the classes and axioms of the ontology formulation, which is based on the Web Ontology Language (OWL). The technical implementation and functionality of OBDM are demonstrated and validated with test results from raw material, geometric models, process parameters, tensile, fatigue, compression, density, bending, and thermal tests. The approach is not limited to a single additive manufacturing printing process and can be extended to other processes. Exemplary applications to Wire Arc Additive Manufacturing (WAAM) and Composite Extrusion Modeling (CEM) are demonstrated.