| 1. |
- Bae, Kichang, et al.
(författare)
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Corrosion Resistance of Laser Powder Bed Fused AISI 316L Stainless Steel and Effect of Direct Annealing
- 2022
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Ingår i: Materials. - : MDPI. - 1996-1944. ; 15:18
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Tidskriftsartikel (refereegranskat)abstract
- Alloy parts produced by an additive manufacturing method with rapid heat transfer from fast melting and solidification have different microstructures, characteristics, and performances compared with materials made by the conventional process. In this study, the corrosion and oxidation resistance of SS316L, which was prepared by the powder bed fusion process, was compared with those of cold-rolled SS316L. Additionally, the surface oxide film on stainless steel was thoroughly assessed since the film has the greatest influence on the corrosion and oxidation resistance. The effect of heat treatment on corrosion and oxidation resistance of SS316L fabricated by additive manufacturing was investigated. The SS316L has a microstructure formed by sub-grain cells, in which locally concentrated alloying elements form a stable passive film. As a result, it has a higher level of corrosion resistance and oxidation resistance than conventional cold-rolled materials. However, it was confirmed that the sub-grain cell was removed by heat treatment, which resulted in the degradation of corrosion and oxidation resistance.
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| 2. |
- Cremades, Andrés, et al.
(författare)
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Identifying regions of importance in wall-bounded turbulence through explainable deep learning
- 2024
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Ingår i: Nature Communications. - : Nature Research. - 2041-1723. ; 15:1
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Tidskriftsartikel (refereegranskat)abstract
- Despite its great scientific and technological importance, wall-bounded turbulence is an unresolved problem in classical physics that requires new perspectives to be tackled. One of the key strategies has been to study interactions among the energy-containing coherent structures in the flow. Such interactions are explored in this study using an explainable deep-learning method. The instantaneous velocity field obtained from a turbulent channel flow simulation is used to predict the velocity field in time through a U-net architecture. Based on the predicted flow, we assess the importance of each structure for this prediction using the game-theoretic algorithm of SHapley Additive exPlanations (SHAP). This work provides results in agreement with previous observations in the literature and extends them by revealing that the most important structures in the flow are not necessarily the ones with the highest contribution to the Reynolds shear stress. We also apply the method to an experimental database, where we can identify structures based on their importance score. This framework has the potential to shed light on numerous fundamental phenomena of wall-bounded turbulence, including novel strategies for flow control.
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