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The Convolutional Multiple Whole Profile (CMWP) Fitting Method, a Global Optimization Procedure for Microstructure Determination,Crystals
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The analysis of line broadening in X-ray and neutron diffraction patterns using profile functions constructed on the basis of well-established physical principles and TEM observations of lattice defects has proven to be a powerful tool for characterizing microstructures in crystalline materials. These principles are applied in the convolutional multiple-whole-profile (CMWP) procedure to determine dislocation densities, crystallite size, stacking fault and twin boundary densities, and intergranular strains. The different lattice defect contributions to line broadening are separated by considering the hkl dependence of strain anisotropy, planar defect broadening and peak shifts, and the defect dependent profile shapes. The Levenberg–Marquardt (LM) peak fitting procedure can be used successfully to determine crystal defect types and densities as long as the diffraction patterns are relatively simple. However, in more complicated cases like hexagonal materials or multiple-phase patterns, using the LM procedure alone may cause uncertainties. Here, we extended the CMWP procedure by including a Monte Carlo statistical method where the LM and a Monte Carlo algorithm were combined in an alternating manner. The updated CMWP procedure eliminated uncertainties and provided global optimized parameters of the microstructure in good correlation with electron microscopy methods.
中文翻译：
使用轮廓函数分析X射线和中子衍射图谱中的谱线展宽，该轮廓函数是基于已建立的物理原理和TEM观察到的晶格缺陷而构造的，已被证明是表征晶体材料微结构的有力工具。这些原理应用于卷积多轮廓（CMWP）过程中，以确定位错密度，微晶尺寸，堆垛层错和孪晶边界密度以及晶间应变。通过考虑hkl可以分离出不同的晶格缺陷对线宽的贡献应变各向异性，平面缺陷扩展和峰位移以及与缺陷有关的轮廓形状。只要衍射图样相对简单，Levenberg-Marquardt（LM）峰拟合程序就可以成功地用于确定晶体缺陷的类型和密度。但是，在更复杂的情况下，例如六角形材料或多相图案，仅使用LM程序可能会导致不确定性。在这里，我们通过包括蒙特卡洛统计方法（其中将LM和蒙特卡洛算法以交替方式组合）的扩展了CMWP过程。更新的CMWP程序消除了不确定性，并提供了与电子显微镜方法良好相关的微观结构的全局优化参数。 |
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