| About this Abstract |
| Meeting |
MS&T23: Materials Science & Technology
|
| Symposium
|
Additive Manufacturing Modeling, Simulation, and Machine Learning: Microstructure, Mechanics, and Process
|
| Presentation Title |
Utilizing Cellular Automata to Resolve Process Parameter to Microstructure Correlations in LPBF Additively Manufactured Parts |
| Author(s) |
Michael M. Fazzino, Serge Nakhmanson, Rainer Hebert, Lukasz Kuna |
| On-Site Speaker (Planned) |
Michael M. Fazzino |
| Abstract Scope |
This work utilizes a Cellular Automata Finite Element (CAFE) method, developed at NRL, together with a a process parameter sensitive enriched analytical solution method for temperature fields to predict the microstructure of 316L stainless steel produced through additive manufacturing (AM). This approach allows one to precisely model melt pool characteristics utilizing process parameters, such as power, speed, and raster pattern, to evaluate microstructure characteristics including grain texture, and morphology. The CAFE model was first validated using experimental data from a selective laser melting (SLM) process, including the melt pool dimensions, and resulting grain structure. Next, simulations were carried out on representative volume elements to evaluate correlations between various processing parameters and the influence on grain morphology, boundaries, and texture. We believe this research will contribute to the wider development of AM techniques and provide insights for industry professionals. |