Abstract
The booming and evolution of additive manufacturing (AM) technologies call for robust key enabling technologies and solutions to the ongoing advancement of AM. However, there are limitations to the fused deposition modeling-based design for AM (FDM-based DFAM), including an inadequate understanding of the process activities and the progressive industrialization, which make the concept generation operations unreliable, inconsistent, and of limited influence. This paper proposes a principle knowledge-based framework for enabling technologies in FDM-based DFAM to provide solutions to the abovementioned engineering problems to increase the viability of industrial applications. Consequently, a case study application is used to verify the feasibility and effectiveness of our approach.
Original language | English |
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Article number | 012087 |
Journal | Journal of Physics: Conference Series |
Volume | 2762 |
Issue number | 1 |
DOIs | |
State | Published - 2024 |
Event | 2023 International Symposium on Structural Dynamics of Aerospace, ISSDA 2023 - Xi'an, China Duration: 9 Sep 2023 → 10 Sep 2023 |
Keywords
- Bayesian Network
- Design Process
- Fused Deposition Modeling
- Knowledge Graph
- UML Modeling