Event Knowledge Graph for a Knowledge-Based Design Process Model for Additive Manufacturing - Chen Guohui; Auwal Haruna; Chen Youze; Li Lunyong; Khandaker Noman; Yongbo Li; K. Eliker (Machines)
Additive manufacturing (AM) technology is gaining acceptance as a strategic manufacturing technique for allowing new product development. Due to ongoing process improvement, design for AM (DFAM) has become a major issue in harnessing AM’s production and development possibilities to achieve design freedom. The classical design process model does not encompass all the knowledge available to take advantage of design freedom. Therefore, a conceptual and in-depth analysis of design alternatives is necessary to determine the manufacturing process. As a result, this research proposed a design process model for a DFAM to attain design freedom with a unique approach and resource selection steps for fused deposition modeling (FDM) that uses an information model based on evolving knowledge and addressing the challenges. The proposed design process model uses an event knowledge graph (EKG) to outline the product manufacturability from the perspective of DFAM limitations. Event-based knowledge representation provides causality information for knowledge-based reasoning in causality analysis tasks. A relationship-aware mechanism is then used to express events on the graph that are directed from entities to occurrences to efficiently extract the most relevant details. Thus, this implements a step-by-step approach to process and resource specifications during the design stage. Consequently, it offers a comprehensive learning approach for establishing and modeling intrinsic relationships to attain flexibility and design freedom. The efficacy and feasibility of the proposed approach are verified by using an application case study of an intake system based on the airflow sensing rate and controls how much air is fed into the engine.