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Vol.9 No.2previous AASP17 (125-126-127-128-129-130-131-132-133-134-135-136-137-138-139-140-141-142) NT85

Academic Articles
Regular Paper Vol.9 No.2 (2017) p.38 - p.43
 

A Solution of Atom Map Data Analysis for Atom Probe Tomography Training

 
Dongyue CHEN 1,*, Kenta MURAKAMI2, Kenji NISHIDA3, Zhengcao LI4,*, Naoto SEKIMURA1
 

1 The University of Tokyo, Dept. of Nuclear Engineering and Management, Japan
2 The University of Tokyo, Nuclear Professional School, School of Engineering, Japan
3 Central Research Institute of Electric Power Industry, Japan
4 Tsinghua University, School of Materials Science and Engineering, China

 
Abstract
Atom probe tomography is a promising tool for future ageing management of nuclear power plants. For a wider application of atom probe technique in the future, a training program for university students and young researchers has been proposed, and a practice section has been designed to provide chances to experience the data analysis of atom probe. However, the available commercial software for the data analysis is expensive and lacks enough flexibility. In this work, a data analysis solution for atom probe with free codes and free software has been set up. The requirement of atom map visualization has been discussed, and the atom map data has been successfully visualized with the open-source software ParaView. A precipitate searching code has been developed based on the maximum separation method, which is the most popular method currently. The precipitate searching code consists of three steps: maximum separation step, envelope step and erosion step. The detailed algorithm of the code has been discussed, and the effects of tuning the precipitate searching parameters in the code have been demonstrated. With this set of tools, the training participants could get a vivid picture of the advantages and limitations of the atom probe technique.
 
Keywords
Atom probe tomography, Training, Precipitate searching, Maximum separation method
 
Full Paper: PDF
Article Information
Article history:
Received 12 October 2016
Accepted 29 May 2017