کد مقاله کد نشریه سال انتشار مقاله انگلیسی نسخه تمام متن
10672469 1009859 2015 7 صفحه PDF دانلود رایگان
عنوان انگلیسی مقاله ISI
Extracting features buried within high density atom probe point cloud data through simplicial homology
ترجمه فارسی عنوان
ویژگی های استخراج در داده های ابر نقطه ای پروب نقطه با استفاده از هماهنگی ساده می شود
موضوعات مرتبط
مهندسی و علوم پایه مهندسی مواد فناوری نانو (نانو تکنولوژی)
چکیده انگلیسی
Feature extraction from Atom Probe Tomography (APT) data is usually performed by repeatedly delineating iso-concentration surfaces of a chemical component of the sample material at different values of concentration threshold, until the user visually determines a satisfactory result in line with prior knowledge. However, this approach allows for important features, buried within the sample, to be visually obscured by the high density and volume (~107 atoms) of APT data. This work provides a data driven methodology to objectively determine the appropriate concentration threshold for classifying different phases, such as precipitates, by mapping the topology of the APT data set using a concept from algebraic topology termed persistent simplicial homology. A case study of Sc precipitates in an Al-Mg-Sc alloy is presented demonstrating the power of this technique to capture features, such as precise demarcation of Sc clusters and Al segregation at the cluster boundaries, not easily available by routine visual adjustment.
ناشر
Database: Elsevier - ScienceDirect (ساینس دایرکت)
Journal: Ultramicroscopy - Volume 159, Part 2, December 2015, Pages 374-380
نویسندگان
, , , ,