A data-driven framework to predict fused filament fabrication part properties using surrogate models and multi-objective optimisation

• Main Authors: Zhang, Yongjie, Choi, Joon Phil, Moon, Seung Ki
• 其他作者: School of Mechanical and Aerospace Engineering
• 格式: Article
• 語言: English
• 出版: 2022
• 主題: Engineering::Mechanical engineering; Additive Manufacturing; Fused Filament Fabrication
• 在線閱讀: https://hdl.handle.net/10356/161922
• 機構: Nanyang Technological University

In additive manufacturing (AM), due to large number of process parameters and multiple responses of interest, it is hard for AM designers to attain optimal part performance without a systematic approach. In this research, a data-driven framework is proposed to achieve the desired AM part performance...