| About this Abstract |
| Meeting |
MS&T24: Materials Science & Technology
|
| Symposium
|
Additive Manufacturing of Titanium-based Materials: Processing, Microstructure and Material Properties
|
| Presentation Title |
Machine Learning Enabled Discovery of New L-PBF Processing Domains for Ti-6Al-4V |
| Author(s) |
Timothy Montalbano, Salahudin Nimer, Brendan Croom, Mary Daffron, Somnath Ghosh, Morgana Trexler, Steven Storck |
| On-Site Speaker (Planned) |
Timothy Montalbano |
| Abstract Scope |
While it is well established that the microstructure and mechanical properties of Ti-6Al-4V alloys are sensitive to their thermal history, a relatively small portion of the processing space has been explored through Edisonian experiments. This implies that there are new processing domains that could yield Ti-6Al-4V with favorable mechanical properties or improved build productivity. Using efficient X-ray Computed Tomography methods to quickly screen the density, and subscale tension tests to identify mechanical properties of L-PBF Ti-6Al-4V, we leveraged Gaussian Process Regression (GPR) machine learning models to efficiently explore the L-PBF processing space to discover new processing domains. The new processing conditions resulted in extreme microstructural variation with differences in grain morphology, size, and texture. The degree of texture and grain refinement exhibited in the reconstructed β-grains of some samples partially explained the large amount of ductility and variations in strength in the tested samples. |