About this Abstract |
Meeting |
2023 TMS Annual Meeting & Exhibition
|
Symposium
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AI/Data Informatics: Computational Model Development, Validation, and Uncertainty Quantification
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Presentation Title |
Active Learning of Powder Milling Machine for Optimized Silicon Particle Size Control |
Author(s) |
Jong Ho Kim |
On-Site Speaker (Planned) |
Jong Ho Kim |
Abstract Scope |
Silicon powder has recently started to be used as a raw material for anode materials for lithium ion batteries. Silicon powder has a higher capacity than graphite, but has a brittle characteristic, so there is a limit to its usage. One of the prerequisites for applying the silicon powder is to control the particle size of the powder. In this study, a traditional milling device and machine learning were combined to control the particle size of silicon powder. In particular, in order to obtain a target particle size, an efficient optimization method was proposed by applying Bayesian inference and active learning methods. It was confirmed that the combination of traditional experimental equipment and machine learning is an efficient silicon powder particle size control method. |
Proceedings Inclusion? |
Planned: |
Keywords |
Machine Learning, Powder Materials, Process Technology |