A Framework for FDM-based DFAM: Key Enabling Technologies for Knowledge-based Design

Auwal Haruna, Khandaker Noman, Yongbo Li, Intizar Ali Shah

Research output: Contribution to journalConference articlepeer-review

1 Scopus citations

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 languageEnglish
Article number012087
JournalJournal of Physics: Conference Series
Volume2762
Issue number1
DOIs
StatePublished - 2024
Event2023 International Symposium on Structural Dynamics of Aerospace, ISSDA 2023 - Xi'an, China
Duration: 9 Sep 202310 Sep 2023

Keywords

  • Bayesian Network
  • Design Process
  • Fused Deposition Modeling
  • Knowledge Graph
  • UML Modeling

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