Reza Tangestani, Trevor Sabiston, Apratim Chakraborty, Waqas Muhammad, Yuan Lang and Étienne Martin
Article (2021)
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Abstract
This is the first of two manuscripts that presents a computationally efficient full field deterministic model for laser powder bed fusion (LPBF). A new Hybrid Line (HL) heat input model integrates an exponentially decaying (ED) heat input over a portion of a laser path to significantly reduce the computational time. Experimentally measured properties of the high gamma prime nickel-based superalloy RENe 65 are implemented in the model to predict the in-process temperature distribution, stresses, and distortions. The model accounts for specific properties of the material as different phases. The first manuscript presents the HL heat transfer model, which is compared with the beam-scale exponentially decaying model, along with the melt pool geometry obtained experimentally by varying the laser parameters. The predicted melt pool geometry of the beam-scale ED model is shown to have good agreement with experimental measurements. While the proposed HL model exhibits lesser accuracy in predicting the melt pool geometries, it can predict the cooling rates and nodal temperatures as accurately as to the ED model. Moreover, under large time integration steps, the HL model becomes more than 1,500 times faster than the ED model.
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Department: | Department of Mechanical Engineering |
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Funders: | CRSNG/NSERC |
Grant number: | RGPIN2019-04073 |
PolyPublie URL: | https://publications.polymtl.ca/53239/ |
Journal Title: | Frontiers in Materials (vol. 8) |
Publisher: | Frontiers Media S.A. |
DOI: | 10.3389/fmats.2021.753040 |
Official URL: | https://doi.org/10.3389/fmats.2021.753040 |
Date Deposited: | 18 Apr 2023 15:00 |
Last Modified: | 09 Apr 2025 11:33 |
Cite in APA 7: | Tangestani, R., Sabiston, T., Chakraborty, A., Muhammad, W., Lang, Y., & Martin, É. (2021). An Efficient Track-Scale Model for Laser Powder Bed Fusion Additive Manufacturing: Part 1- Thermal Model. Frontiers in Materials, 8, 753040 (14 pages). https://doi.org/10.3389/fmats.2021.753040 |
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