Global optimum of microstructure parameters in the CMWP line-profile-analysis method by combining Marquardt-Levenberg and Monte-Carlo procedures
Global optimum of microstructure parameters in the CMWP line-profile-analysis method by combining Marquardt-Levenberg and Monte-Carlo procedures
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DOI：
10.1016/j.jmst.2019.01.014
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发表时间：
2019-06
影响因子：
10.9
通讯作者：
G. Ribárik;B. Jóni;T. Ungár
中科院分区：
文献类型：
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作者：
G. Ribárik;B. Jóni;T. Ungár
X 射线和中子衍射图谱的线轮廓分析是确定晶体材料微观结构的有力工具。卷积多重整体轮廓 (CMWP) 过程基于位错、域尺寸、堆垛层错和孪生边界的物理轮廓函数。使用阶次依赖性、应变各向异性、平面缺陷的hkl依赖性展宽和峰形状来分离不同晶格缺陷类型的影响。 Marquardt-Levenberg (ML) 数值优化程序已成功用于确定晶体缺陷类型和密度。然而，在更复杂的情况下，例如六边形材料或多相，仅机器学习程序就揭示了不确定性。在一种新方法中，机器学习和蒙特卡罗统计方法以另一种方式结合起来。新的 CMWP 程序消除了不确定性并提供了全局优化的微观结构参数。
Line profile analysis of X-ray and neutron diffraction patterns is a powerful tool for determining the microstructure of crystalline materials. The Convolutional-Multiple-Whole-Profile (CMWP) procedure is based on physical profile functions for dislocations, domain size, stacking faults and twin boundaries. Order dependence, strain anisotropy,hkldependent broadening of planar defects and peak shape are used to separate the effect of different lattice defect types. The Marquardt-Levenberg (ML) numerical optimization procedure has been used successfully to determine crystal defect types and densities. However, in more complex cases like hexagonal materials or multiple phases the ML procedure alone reveals uncertainties. In a new approach the ML and a Monte-Carlo statistical method are combined in an alternative manner. The new CMWP procedure eliminates uncertainties and provides globally optimized parameters of the microstructure.