Design for additive manufacturing knowledgebase development and its application for material extrusion

Material extrusion is one of widely applied additive manufacturing techniques to fabricate polymer parts with high geometrical complexity. Fundamental factors have been well developed for material extrusion process, which determines performance and quality of final parts, such as geometry accuracy, surface roughness, and microstructure. However, decision making for process selection heavily depends on experts' domain knowledges for supporting the decision making. Since it is challenging to collect and formalize experts' domain knowledges that are scattered, it requires knowledgebase to store and retrieve the experts' domain knowledge for reusing. This study proposes a design for additive manufacturing (DfAM) ontology to store metadata of the material extrusion process for selecting appropriate AM processes depending on manufacturing capabilities. The DfAM ontology is developed based on the metadata including material, process of fabrication, geometry information, and their relationships. The DfAM ontology significantly contributes to determining which process parameters affect the quality of final parts. To demonstrate the usefulness of the DfAM ontology, a case study was performed to select an AM machine based on the manufacturing capabilities.