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-Based Thermal Analysis for Laser-Based Powder-Bed Fusion Additive Manufacturing |
Author(s) |
Raeita Mehraban Teymouri, Ardalan Sofi, Chinnapat Panwisawas, Bahram Ravani |
On-Site Speaker (Planned) |
Raeita Mehraban Teymouri |
Abstract Scope |
Laser-based powder-bed fusion additive manufacturing (L-PBF AM) processes involve complex micro time scale and length scale phenomena. Computationally expensive high-fidelity thermo-fluid simulations and efficient yet over-simplified conduction-only models are two main categories of existing thermal modeling techniques for L-PBF AM processes. However, there is a pressing need to develop a Reduced-Order Model (ROM) that would account for lower length scale phenomena while allowing to scale up to larger domains for L-PBF AM applications. This ROM can then conveniently be used to develop a dataset to train a Machine Learning (ML) model capable of performing an accurate near-real-time evaluation of the temperature field of additively manufactured parts at any stage of the manufacturing process. Moreover, one of the major objectives of this work is developing process maps using key process variables to understand the composition-process relationships and determine the optimum process window for L-PBF AM of widely used commercial alloys. |
Proceedings Inclusion? |
Undecided |