期刊
NPJ COMPUTATIONAL MATERIALS
卷 8, 期 1, 页码 -出版社
NATURE PORTFOLIO
DOI: 10.1038/s41524-022-00866-9
关键词
-
资金
- Harbin Institute of Technology
- Pennsylvania State University, University Park, PA
Machine learning algorithms combined with human intelligence and physics-based models improve the computational efficiency and problem tractability in 3D printing of fully dense superior metallic parts. A verifiable quantitative index and visual process maps are provided to achieve fully dense superior parts and improve quality consistency.
Machine learning algorithms are a natural fit for printing fully dense superior metallic parts since 3D printing embodies digital technology like no other manufacturing process. Since traditional machine learning needs a large volume of reliable historical data to optimize many printing variables, the algorithm is augmented with human intelligence derived from the rich knowledge base of metallurgy and physics-based models. The augmentation improves the computational efficiency and makes the problem tractable by enabling the algorithm to use a small set of data. We provide a verifiable quantitative index for achieving fully dense superior parts, facilitate material selection, uncover the hierarchy of important variables that affect the density, and present easy-to-use visual process maps. These findings can improve the quality consistency of 3D printed parts that now limit their greater industrial adaptation. The approach used here can be applied to solve other problems of 3D printing and beyond.
作者
我是这篇论文的作者
点击您的名字以认领此论文并将其添加到您的个人资料中。
推荐
暂无数据