| About this Abstract |
| Meeting |
2023 Annual International Solid Freeform Fabrication Symposium (SFF Symp 2023)
|
| Symposium
|
2023 Annual International Solid Freeform Fabrication Symposium (SFF Symp 2023)
|
| Presentation Title |
Pushing Boundaries: Machine Learning Applied to Selective Laser Melting |
| Author(s) |
Mary E. Daffron, Steven Storck, Brendan Croom, Timothy Montalbano, Salahudin Nimer |
| On-Site Speaker (Planned) |
Mary E. Daffron |
| Abstract Scope |
A primary concern in advancing selective laser melting (SLM) is developing a method to rapidly establish laser parameters for the end application. Establishing a set of processing parameters is complicated by the infinite number of possible combinations of machine variables. Additionally, certain processing domains can be unstable resulting in failure to extract valuable statistical information. Leveraging total machine capacity while balancing laser processing parameters is vital to the scalability of AM at quality. Intelligent parameter development via machine learning techniques unlocks the full processing space to enable unrealized potential including application specific properties. Rapid characterization techniques combined with strategic evaluation of microstructure inform the machine learning model and parameter development process. This results in a thorough exploration of the processing space in fewer build cycles. Examples will be presented showing the identification of new processing domains with increased density and targeted mechanical behavior optimization compared manufacturer recommended parameter sets. |
| Proceedings Inclusion? |
Definite: Post-meeting proceedings |