| About this Abstract |
| Meeting |
2025 TMS Annual Meeting & Exhibition
|
| Symposium
|
Aluminum Alloys: Development and Manufacturing
|
| Presentation Title |
Two-Phase Flow Simulation for Distinguishing Deformable Particles with a LiMCA System |
| Author(s) |
Xiaodong Wang, Xiaokang Guo, Fuhai Wang, Mihaiela Isac, Roderick Guthrie |
| On-Site Speaker (Planned) |
Xiaodong Wang |
| Abstract Scope |
In the metallurgical industry, Liquid Metal Cleanliness Analyser (LiMCA) commercial equipment cannot distinguish between hard particles (e.g., oxides, borides) and deformable particles (e.g., bubbles, molten salts). Hard particle concentrations can sometimes be grossly overestimated, which reduces the measurement accuracy. In the present study, a mathematical model is developed to distinguish deformable particles from hard particles. The deformation of particles under mixed extensional and shear flow is studied by using the conservative level-set (CLS) method. The effect of a particle’s deformation and the feasibility to discriminate it from non-deformable particles in the LiMCA system is evaluated. Furthermore, an unsupervised classification algorithm balanced iterative reducing and clustering using hierarchies clustering is employed to distinguish the pulsed features between hard particles and bubbles using a self-develop system: OiMPA (Online Micron-sized Particle Analyzer). This study scheme has a guiding and facilitating role in applying the LiMCA system to the industrial online measurement environment. |
| Proceedings Inclusion? |
Planned: Light Metals |
| Keywords |
Aluminum, Machine Learning, Process Technology |