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Meeting TMS Specialty Congress 2024
Symposium 2nd World Congress on Artificial Intelligence in Materials & Manufacturing (AIM 2024)
Presentation Title Data Driven Modeling for Yield Improvement in Gas Atomization Process
Author(s) Michael Ridenhour, Shankarjee Krishnamoorthi, David Bryan, Darryl Glanton, John Goetz
On-Site Speaker (Planned) Michael Ridenhour
Abstract Scope Physics-based modeling is an invaluable tool for understanding the impact of process parameters on production quality and efficiency. However, in the case of gas atomization, physics-based modelling is immature and computationally impractical due to runtime and memory limitations. We offer an alternate approach utilizing machine learning techniques to identify critical process parameters as they relate to powder yield, which we define as the percentage of particles under a certain size, of Ni-based superalloys. We relax the problem and treat a deep-learning model trained to predict yield based on high-speed process data from our atomizer as a proxy for the physical process. We then analyze this model to determine which process parameters have the highest impact on our prediction values and offer that these parameters are similarly impactful in the physical process as well. The identified process parameters matched with parameters typically identified in literature and based on operator experience.
Proceedings Inclusion? Definite: Other

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