About this Abstract |
Meeting |
MS&T22: Materials Science & Technology
|
Symposium
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Additive Manufacturing Modeling, Simulation, and Machine Learning: Microstructure, Mechanics, and Process
|
Presentation Title |
Using Machine Learning to Characterize Powder Behavior and Surface Roughness in Powder Bed Fusion AM |
Author(s) |
Srujana Rao Yarasi, Elizabeth Holm, Anthony Rollett |
On-Site Speaker (Planned) |
Srujana Rao Yarasi |
Abstract Scope |
In powder bed fusion additive manufacturing (AM), characteristics of the powder feedstock such as particle size distribution, sphericity, and morphology affect the flowability of the powder and the layer density distribution of the powder bed. The use of computer vision and machine learning tools in the AM domain have enabled the quantitative investigation of qualitative factors like powder morphology. The use of Convolutional Neural Networks (CNN) to generate feature descriptors is proposed as part of a framework to generate powder morphology distributions that describe morphological characteristics of the powder. Similarly, the measurement of surface roughness is an image data-rich problem that can benefit from characterization with machine learning techniques. There are multiple factors that affect surface roughness including powder size and process parameters. ML techniques are used to understand the correlation between these different factors and surface roughness metrics. |