About this Abstract |
| Meeting |
MS&T25: Materials Science & Technology
|
| Symposium
|
Additive Manufacturing Modeling, Simulation, and Machine Learning: Microstructure, Mechanics, and Process
|
| Presentation Title |
Machine Learning-Aided Optimization for Laser-Based AM: Poweder Selection |
| Author(s) |
Yu Zou |
| On-Site Speaker (Planned) |
Yu Zou |
| Abstract Scope |
This presentation will focus on the following optimization aspects using machine learning: (1) The individual influence of particle size distribution (PSD) on the powder flowability has been investigated. To reduce the time and effort required to characterize powder flowability, a reliable computer vision approach is established to evaluate powder flowability based on scanning electron microscope images. (2) ML methods are applied for the parameter optimization by introducing a data-driven framework to establish process maps and suggest optimal processing parameters. (3) To facilitate efficient data analysis for synchrotron X-Ray monitoring, various deep learning models are trained to identify and analyze the keyholes and generated pores from captured images. (4) To delve into the laser-metal interaction process, the ML-predicted laser absorptance is integrated into a computational fluid dynamic model to accurately predict keyhole depth across various processing parameters. |