About this Abstract |
Meeting |
2022 Annual International Solid Freeform Fabrication Symposium (SFF Symp 2022)
|
Symposium
|
2022 Annual International Solid Freeform Fabrication Symposium (SFF Symp 2022)
|
Presentation Title |
Machine Learning-Assisted Prediction of Thermophysical Properties of Nickel-Base Alloys over a Temperature Range |
Author(s) |
Sudeepta Mondal, Nandana Menon, Asok Ray, Amrita Basak |
On-Site Speaker (Planned) |
Nandana Menon |
Abstract Scope |
First-principal calculation of thermophysical properties of complex alloys as functions of composition and temperature is computationally expensive. The presence of multiple elements in complex alloys often results in numerical instabilities while such calculations. Furthermore, complex multi-component alloys often exhibit a range of melting temperatures (e.g., solidus and liquidus temperatures), as opposed to a single melting point. To circumvent these challenges, this paper proposes an alternative approach that implements a Gaussian Process (GP) framework to predict thermophysical properties (e.g., bulk density, solidus/liquidus temperatures) of a nickel-base metallic alloy system, nickel-chromium-aluminum (Ni-Cr-Al), over a temperature range. The results show that the proposed GP-based framework is conducive to predicting thermophysical properties with a significantly lower computational overhead and, thus, can be implemented as a surrogate in the digital twin development of additive manufacturing processes. |
Proceedings Inclusion? |
Definite: Post-meeting proceedings |