.png){kind=icon}
返回
A Method for Predicting Powder Flowability for Selective Laser Sintering
.png){kind=icon}
PRE
AI
DOI：10.1007/s11837-021-05050-w.png)
.png){kind=icon}
原文求助.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
This work investigates a method for pre-screening material systems for selective laser sintering using a combination of revolution powder analysis (RPA) and machine learning. To develop this method, nylon was mixed with alumina or carbon fibers in different wt.% to form material systems with varying flowability. The materials were measured in a custom RPA device and the results compared with as-spread layer density and surface roughness. Machine learning was used to attempt classification of all powders for each method. Ultimately, it was found that the RPA method is able to reliably classify powders based on their flowability, but as-spread layer density and surface roughness were not able to be classified.
期刊
IF:
2.3
论文数:
800
被引数:
1.7W
机构
引用论文
.png){kind=icon}
Density improvement of alumina parts produced through selective laser sintering of alumina-polyamide composite powder通过选择性激光烧结氧化铝-聚酰胺复合粉末生产的氧化铝零件的密度提高
PREAI
分享收藏Distinct roles of DNA polymerases delta and epsilon at the replication fork inXenopusegg extracts
OAAI
OAAI分享收藏 没有更多内容
没有更多内容