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Acta Metall Sin  2018, Vol. 54 Issue (6): 868-876    DOI: 10.11900/0412.1961.2017.00318
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Morphological Characteristics and Size Distributions of Three-Dimensional Grains and Grain Boundaries in 316L Stainless Steel
Tingguang LIU1,2(), Shuang XIA2, Qin BAI2, Bangxin ZHOU2
1 National Center for Materials Service Safety, University of Science and Technology Beijing, Beijing 100083, China
2 School of Materials Science and Engineering, Shanghai University, Shanghai 200072, China
Cite this article: 

Tingguang LIU, Shuang XIA, Qin BAI, Bangxin ZHOU. Morphological Characteristics and Size Distributions of Three-Dimensional Grains and Grain Boundaries in 316L Stainless Steel. Acta Metall Sin, 2018, 54(6): 868-876.

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Abstract  

Three-dimensional characterization of grains and grain boundaries is significant to study the microstructure of polycrystalline materials, and is the key to advance the subject of three-dimensional materials science (3DMS). In this work, the technique of serial sectioning by mechanical polishing coupled with 3D electron backscatter diffraction (3D-EBSD) mapping was used to measure the microstructure of a 316L stainless steel in 3D. Volume of the collected 3D-EBSD microstructure is 600 μm×600 μm×257.5 μm, which is quite large to study the 3D microstructure of structural materials with conventional grain size (20~60 μm). Dream3D and in-house developed Matlab programs were used to process the 3D-EBSD data, and subsequently ParaView was used to visualize the grains and grain boundaries in 3D. Combined usage of these tools and in-house programs make the possibility that not only 3D grains but also 3D grain boundaries can be studied in both morphology and quantification. In total, 1840 grains and 9177 grain boundaries are included in the measured 3D-EBSD microstructure. The 3D morphological characteristics and size distributions of grains and grain boundaries in the 316L stainless steel were investigated, including 3D grain size, grain surface area, boundary quantity per grain, grain boundary size and the average boundary size per grain, as well as relationships between these morphological parameters were discussed. Results showed that distributions of all of these morphological parameters of 3D grains and grain boundaries in the polycrystalline 316L steel can be well represented by log-normal distribution, and all relationships of these parameters versus grain size can be well represented by power function. Additionally, the 3D morphologies of most grains in the 316L stainless steel deviate from the ideal equiaxed grains, having complex shapes due to existing of twins, such as semi-sphere shaped, plate shaped and some very complex grains. In many ways, the larger grains have more complex morphology with greater number of faces, larger surface area and larger deviation from equiaxed grains.

Key words:  316L stainless steel      3D-EBSD      3D microstructure      3D grain      3D grain boundary     
Received:  27 July 2017     
ZTFLH:  TG142.1  
Fund: Supported by National Natural Science Foundation of China (No.51671122) and Fundamental Research Funds for the Central Universities (No.FRF-TP-16-041A1)

URL: 

https://www.ams.org.cn/EN/10.11900/0412.1961.2017.00318     OR     https://www.ams.org.cn/EN/Y2018/V54/I6/868

Fig.1  Reconstructed 3D-EBSD microstructures of a 316L stainless steel sample
(a) schematic of specimen and mark for EBSD mapping
(b) the bulk microstructure that colored by using the standard inverse-pole-figure (IPF) color code of direction Z
(c) cross-section map in X-Y-Z directions
(d) visualization of a part of the measured 3D grain boundary network (The grain boundaries were colored according to the angle of misorientation)
(e) the 2D EBSD microstructure of slice 101
Fig.2  3D visualizations of a typical grain (a) from the 316L stainless steel and four boundaries (b~e) on the grain (The grains or boundaries were colored according to the angle of grain boundary misorientations)
Fig.3  Grain size distribution for the 3D-EBSD microstructure of 316L and its log-normal fitting curve (y0, A and w—constants, d—grain diameter, c—the median value in the log-normal distribution)
Fig.4  Statistic of grain surface area for the 3D microstructure and the log-normal fitting curve (The 6 grains with surface area larger than 1.1×105 μm2 are shown separately) (a), relationship between the grain surface area and the grain size and its power function fitting curve, and the curve of sphere surface area to diameter (b)
Fig.5  A morphologically complex large grain g101 in the 3D microstructure of 316L (a) and two relative small grains g1211 (b) and g1026 (c) that are neighbors of the large grain
Fig.6  Statistic of the quantity of boundaries (or faces) per grain (F) for the 3D microstructure and the log-normal fitting curve (a), relationship between the boundary quantity per grain and the grain size and its power function fitting curve (b)
Fig.7  Statistic of equivalent circle diameters of boundaries for the 3D microstructure and the log-normal fitting curve
Fig.8  Statistic of the average boundary diameter per grain for the 3D microstructure and the log-normal fitting curve (a), relationship between the average boundary diameter per grain and the grain size and its power function fitting curve (b)
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