About this Abstract |
Meeting |
1st World Congress on Artificial Intelligence in Materials and Manufacturing (AIM 2022)
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Symposium
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First World Congress on Artificial Intelligence in Materials and Manufacturing (AIM 2022)
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Presentation Title |
Machine Learning Enabled Directed Energy Deposition of Functionally Graded Materials |
Author(s) |
Alex Kitt, Lee Kerwin, Anindya Bhaduri, Luke Mohr, Chen Shen, Siyeong Ju, Hyeyun Song, Shenyan Huang, Arushi Dhakad, Sathyanarayanan. Raghavan, Marissa Brennan, Lang Yuan, Changjie Sun |
On-Site Speaker (Planned) |
Alex Kitt |
Abstract Scope |
When developing functionally graded materials, residual stress, formation of detrimental phases, differences in liquidus temperatures, and crack formation modes must be considered. To overcome these complexities, this work synergies a diverse range of inputs within a Bayesian Hybrid Model. The focus of this work is a low-gamma' to high-gamma' high strength nickel alloy functionally graded materials built using laser blown powder directed energy deposition for gas turbine or jet engine applications.
This presentation will describe how thermodynamic modeling, process modeling, and microstructure modeling are calibrated against process-monitoring, photo-micrographs, neutron scattering, and synchrotron measurements. Further, it will describe how the experimental results and calibrated models are leveraged to enhance the process development. Special focus will be given to data registration and prediction of cooling rates. |
Proceedings Inclusion? |
Undecided |