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Accurate reconstruction of EBSD datasets by a multimodal data approach using an evolutionary algorithm

机译:使用进化算法通过多模数据方法精确重建EBSD数据集

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A new method has been developed for the correction of the distortions and/or enhanced phase differentiation in Electron Backscatter Diffraction (EBSD) data. Using a multi-modal data approach, the method uses segmented images of the phase of interest (laths, precipitates, voids, inclusions) on images gathered by backscattered or secondary electrons of the same area as the EBSD map. The proposed approach then searches for the best transformation to correct their relative distortions and recombines the data in a new EBSD file. Speckles of the features of interest are first segmented in both the EBSD and image data modes. The speckle extracted from the EBSD data is then meshed, and the Covariance Matrix Adaptation Evolution Strategy (CMA-ES) is implemented to distort the mesh until the speckles superimpose. The quality of the matching is quantified via a score that is linked to the number of overlapping pixels in the speckles. The locations of the points of the distorted mesh are compared to those of the initial positions to create pairs of matching points that are used to calculate the polynomial function that describes the distortion the best. This function is then applied to un-distort the EBSD data, and the phase information is inferred using the data of the segmented speckle. Fast and versatile, this method does not require any human annotation and can be applied to large datasets and wide areas. Besides, this method requires very few assumptions concerning the shape of the distortion function. It can be used for the single compensation of the distortions or combined with the phase differentiation. The accuracy of this method is of the order of the pixel size. Some application examples in multiphase materials with feature sizes down to 1 mu m are presented, including Ti-6A1-4 V Titanium alloy, Rene 65 and additive manufactured Inconel 718 Nickel-base superalloys.
机译:已经开发了一种新方法,用于校正电子反向散射衍射(EBSD)数据中的扭曲和/或增强的相位分化。使用多模态数据方法,该方法使用与EBSD地图相同区域的反向散射或二次电子收集的图像上的感兴趣阶段(车床,沉淀,空隙,夹杂物)的分段图像。然后,所提出的方法搜索最佳转换以纠正其相对扭曲并重新组合新的EBSD文件中的数据。感兴趣的特征的斑点首先在EBSD和图像数据模式中进行分段。然后将从EBSD数据提取的斑点网格化,并且实施协方差矩阵适配演化策略(CMA-ES)以使网格扭曲,直到斑点叠加。通过与斑点中的重叠像素的数量相关联的分数来量化匹配的质量。将扭曲网格的点的位置与初始位置的位置进行比较,以创建用于计算最佳的多项式函数的匹配点的匹配点。然后将该函数应用于Un-istort EBSD数据,并且使用分段散斑的数据推断相位信息。快速和多功能,这种方法不需要任何人类注释,并且可以应用于大型数据集和广域。此外,这种方法需要很少有关于失真函数的形状的假设。它可用于对失真的单一补偿或与相位分化组合。该方法的准确性是像素大小的顺序。提出了具有特征尺寸的多相材料中的一些应用实例,包括Ti-6a1-4 V钛合金,ReNe 65和添加剂的Inconel 718镍基超合金。

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