About this Abstract |
Meeting |
2022 TMS Annual Meeting & Exhibition
|
Symposium
|
Materials Design and Processing Optimization for Advanced Manufacturing: From Fundamentals to Application
|
Presentation Title |
Additive Manufacturing of Aluminium: Alloy Design and Machine Learning Assisted Process Optimization |
Author(s) |
Xiaopeng Li, Qian Liu, Jay Kruzic |
On-Site Speaker (Planned) |
Xiaopeng Li |
Abstract Scope |
Laser powder bed fusion technique (LPBF) has been widely used to fabricate various aluminium alloys in the past decade. However, due to intrinsic materials characteristics, e.g., solidification cracking, not many aluminium alloys have satisfactory LPBF processability. Therefore, it is in urgent need to design and develop more suitable aluminium alloys and their composites for LPBF. Meanwhile, once new aluminium alloys are designed, it is also of great importance to optimise the LPBF process to achieve high quality components without any apparent processing defects such as cracks or porosity. In this presentation, a novel in-situ alloy design process was first introduced to develop nanoparticle decorated aluminium alloys for LPBF and the resultant microstructure along with mechanical properties were investigated. Following this, a machine-learning assisted LPBF process optimisation process for aluminium alloys is described in detail to provide new insights into the microstructure control and properties manipulation of LPBF fabricated aluminium alloys. |
Proceedings Inclusion? |
Planned: |
Keywords |
Additive Manufacturing, Aluminum, Machine Learning |