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首页> 外文期刊>ISIJ international >Multiscale Analysis of MnS Inclusion Distributions in High Strength Steel
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Multiscale Analysis of MnS Inclusion Distributions in High Strength Steel

机译：高强度钢中MNS包涵体分布的多尺度分析

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In the present study, manganese sulfide (MnS) inclusions in the high-strength steel were observed by mainly three observation methods (optical microscope, ultrasonic test and serial sectioning) to characterize the size, location and shape distributions across multiple length scales. For the inclusion size, ultrasonic C-scan imaging and three-dimensional internal structure observation with serial sectioning were used to measure the distributions of the square root of the projected area of the inclusion. The obtained size distributions were combined by setting the threshold of ultrasonic amplitude. The validity of the amplitude threshold was verified by observing several inclusions with X-ray CT. The spatial distributions of inclusions were also obtained by the three observation methods, and analyzed on the basis of the coefficient of variation of the mean near-neighbor distance of inclusions (COV_(d)). The results of analyzing COV_(d) in both 2D and 3D spaces revealed that the inclusions in this material were arranged in clusters. For the inclusion shape, the three-dimensional geometries of inclusions were reconstructed from the images obtained by the serial sectioning method, and simplified to ellipsoid by principal component analysis. From the above results, the distributions of inclusion size, aspect ratio and direction (angle between rolling direction and major axis) were successfully obtained. The inclusion distributions were applied to fatigue prediction model, and the fatigue crack initiation life and total fatigue life of the high-strength steel were calculated. The calculation results showed that the multiscale analysis of inclusions would be useful for fatigue life prediction.
机译：在本研究中，通过主要是三个观察方法（光学显微镜，超声波测试和串联切片）来观察高强度钢中的硫化锰（MNS）夹杂物，以表征多个长度尺度的尺寸，位置和形状分布。对于包含尺寸，用连续切片的超声波C扫描成像和三维内部结构观察用于测量夹杂物的投影区域的平方根的分布。通过设定超声幅度的阈值来组合所获得的尺寸分布。通过观察X射线CT的若干夹杂物来验证幅度阈值的有效性。夹杂物的空间分布也通过三种观察方法获得，并根据夹杂物近邻距离的平均近距离的变化系数（CoV_（D））进行分析。在2D和3D空间中分析CoV_（D）的结果显示，该材料中的夹杂物被布置在簇中。对于包含形状，从通过串联切片方法获得的图像重建夹杂物的三维几何形状，并通过主成分分析简化到椭圆形。从上述结果，成功地获得了包含尺寸，纵横比和方向（滚动方向和长轴之间的角度）的分布。将包含分布应用于疲劳预测模型，计算了高强度钢的疲劳裂纹启动寿命和总疲劳寿命。计算结果表明，夹杂物的多尺度分析对于疲劳寿命预测有用。

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