About this Abstract |
Meeting |
2023 TMS Annual Meeting & Exhibition
|
Symposium
|
Additive Manufacturing of Metals: Applications of Solidification Fundamentals
|
Presentation Title |
A-23: Machine Learning Based Parameters Optimization for Selective Laser Melting |
Author(s) |
Jiahui Zhang, Yu Zou |
On-Site Speaker (Planned) |
Jiahui Zhang |
Abstract Scope |
For the selective laser melting process, the printing parameters play a vital role in the mechanical properties of the components while the traditional design of experiments (DOE) method is time-consuming and costly to find the optimized parameters. In this study, we show an easy-deployed and accurate machine learning method to find the possible optimized parameters based on a few experimental results. The microstructure of the components fabricated by random parameters and optimized parameters will be characterized by electron microscopy. The mechanical properties of these components are also compared and analyzed. Our work provides an efficient and reliable method to establish "building process-structure-property" relationships for newly designed materials or alloys. |
Proceedings Inclusion? |
Planned: |
Keywords |
Additive Manufacturing, Machine Learning, Mechanical Properties |