Spectrum imaging and three-dimensional atom probe studies of fine particles in a vanadium micro-alloyed steel

2008 ◽  
Vol 24 (6) ◽  
pp. 641-650 ◽  
Author(s):  
A. J. Craven ◽  
M. MacKenzie ◽  
A. Cerezo ◽  
T. Godfrey ◽  
P. H. Clifton
2020 ◽  
Vol 117 (6) ◽  
pp. 615
Author(s):  
Ping Shen ◽  
Lei Zhou ◽  
Qiankun Yang ◽  
Zhiqi Zeng ◽  
Kenan Ai ◽  
...  

In 38MnVS6 steel, the morphology of sulfide inclusion has a strong influence on the fatigue life and machinability of the steel. In most cases, the MnS inclusions show strip morphology after rolling, which significantly affects the steel quality. Usually, the MnS inclusion with a spherical morphology is the best morphology for the steel quality. In the present work, tellurium was applied to 38MnVS6 micro-alloyed steel to control the MnS inclusion. Trace tellurium was added into 38MnVS6 steel and the effect of Te on the morphology, composition, size and distribution of MnS inclusions were investigated. Experimental results show that with the increase of Te content, the equivalent diameter and the aspect ratio of inclusion decrease strikingly, and the number of inclusions with small aspect ratio increases. The inclusions are dissociated and spherized. The SEM-EDS analysis indicates that the trace Te mainly dissolves in MnS inclusion. Once the MnS is saturated with Te, MnTe starts to generate and wraps MnS. The critical Te/S value for the formation of MnTe in the 38MnV6 steel is determined to be approximately 0.075. With the increase of Te/S ratio, the aspect ratio of MnS inclusion decreases and gradually reaches a constant level. The Te/S value in the 38MnVS6 steel corresponding to the change of aspect ratio from decreasing to constant ranges from 0.096 to 0.255. This is most likely to be caused by the saturation of Te in the MnS inclusion. After adding Te in the steel, rod-like MnS inclusion is modified to small inclusion and the smaller the MnS inclusion, the lower the aspect ratio.


2009 ◽  
Vol 57 (15) ◽  
pp. 4463-4472 ◽  
Author(s):  
Y.M. Chen ◽  
T. Ohkubo ◽  
M. Ohta ◽  
Y. Yoshizawa ◽  
K. Hono

2021 ◽  
Vol 7 (1) ◽  
Author(s):  
Yue Li ◽  
Xuyang Zhou ◽  
Timoteo Colnaghi ◽  
Ye Wei ◽  
Andreas Marek ◽  
...  

AbstractNanoscale L12-type ordered structures are widely used in face-centered cubic (FCC) alloys to exploit their hardening capacity and thereby improve mechanical properties. These fine-scale particles are typically fully coherent with matrix with the same atomic configuration disregarding chemical species, which makes them challenging to be characterized. Spatial distribution maps (SDMs) are used to probe local order by interrogating the three-dimensional (3D) distribution of atoms within reconstructed atom probe tomography (APT) data. However, it is almost impossible to manually analyze the complete point cloud (>10 million) in search for the partial crystallographic information retained within the data. Here, we proposed an intelligent L12-ordered structure recognition method based on convolutional neural networks (CNNs). The SDMs of a simulated L12-ordered structure and the FCC matrix were firstly generated. These simulated images combined with a small amount of experimental data were used to train a CNN-based L12-ordered structure recognition model. Finally, the approach was successfully applied to reveal the 3D distribution of L12–type δ′–Al3(LiMg) nanoparticles with an average radius of 2.54 nm in a FCC Al-Li-Mg system. The minimum radius of detectable nanodomain is even down to 5 Å. The proposed CNN-APT method is promising to be extended to recognize other nanoscale ordered structures and even more-challenging short-range ordered phenomena in the near future.


2018 ◽  
Vol 25 (10) ◽  
pp. 1043-1053 ◽  
Author(s):  
Tao Liu ◽  
Mu-jun Long ◽  
Deng-fu Chen ◽  
Hua-mei Duan ◽  
Lin-tao Gui ◽  
...  

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