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在集群识别算法中的参数优化用于对Al-Mg-Si-Cu合金中的纳米集群进行表征

MinYoung Song¹, Equo Kobayashi¹, JaeHwang Kim²

¹Department of Materials Science and Engineering, Tokyo Institute of Technology, 2-12-1 O-okayama, Meguro-ku, Tokyo 152-8552, Japan.

Microscopy and microanalysis : the official journal of Microscopy Society of America, Microbeam Analysis Society, Microscopical Society of Canada

|June 25, 2024

概括

优化DBSCAN算法参数对于准确地描述合金中的纳米集群至关重要. 这项研究发现,量身定制的参数组合显著减少了人工集群,改善了纳米集群分析.

科学领域:

材料科学材料科学材料科学
计算材料科学科学计算材料科学
金工业是一种金工业.

背景情况:

对合金中纳米集群的准确表征对于了解材料特性至关重要.
基于密度的应用程序与噪音的空间聚类 (DBSCAN) 算法是这种分析的常见工具.
在DBSCAN中用户定义的参数可以显著影响纳米集群识别的准确性.

研究的目的:

优化用于纳米集群表征的DBSCAN算法的用户定义参数 (Dmax,Nmin,顺序 (K)).
为了最大限度地减少Al-0.9%Mg-1.0%Si-0.3%Cu合金样本中非物理集群的形成.
建立一种可靠的方法来确定DBSCAN参数,用于对Al-Mg-Si(-Cu) 合金中的纳米集群分析.

主要方法:

系统优化DBSCAN参数 (Dmax,Nmin,K) 的自然老化 (NA) 和预老化 (PA) 样本.
对集群组成,大小,原子密度和原子排列的分析,以识别和消除非物理集群.
使用体积染和同位表面化验证优化参数以确认高溶液度区域.

主要成果:

识别和量化了四种类型的非物理集群.
确定了最佳的DBSCAN参数组合,以最大限度地减少NA和PA样本的人工集群.
确认优化参数与高溶液度区域保持一致,验证了该方法.

结论:

为每个数据集独立地定制DBSCAN参数优于使用广泛采用的固定参数.
拟议的参数确定策略提高了合金中纳米集群表征的可靠性.
这项工作介绍了纳米集群分析中算法参数选择的强有力的方法.

关键词:

在APT中,APT是APT.这是一种AlMgSiCu合金.集群识别算法集群识别算法纳米集群中的纳米集群.

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