Hostname: page-component-78c5997874-j824f Total loading time: 0 Render date: 2024-11-18T04:29:29.475Z Has data issue: false hasContentIssue false
  • English
  • Français

A Precise Method for Analysis of Elemental Distribution Inside Solute Clusters

Published online by Cambridge University Press:  15 March 2019

Takumi Kitayama ,
Masaya Kozuka ,
Yasuhiro Aruga  and
Chikara Ichihara
Takumi Kitayama*
Affiliation:
Applied Physics Research Laboratory, Kobe Steel Ltd, 1-5-5, Takatsukadai, Nishi-ku, Kobe, Hyogo 651-2271, Japan
Masaya Kozuka
Affiliation:
Material Solutions Division, Kobelco Research Institute, Inc., 1-5-5, Takatsukadai, Nishi-ku, Kobe, Hyogo 651-2271, Japan
Yasuhiro Aruga
Affiliation:
R&D Planning Department, Kobe Steel, Ltd, 2-2-4, Wakinohama-kaigandori, Chuo-ku, Kobe, Hyogo 651-8585, Japan
Chikara Ichihara
Affiliation:
Applied Physics Research Laboratory, Kobe Steel Ltd, 1-5-5, Takatsukadai, Nishi-ku, Kobe, Hyogo 651-2271, Japan
*
*Author for correspondence: Takumi Kitayama, E-mail: kitayama.takumi@kobelco.com
Get access

Abstract

A procedure to analyze the elemental concentration distribution inside solute clusters after detection of clusters from atom probe tomography data set was proposed. We developed a code which can directly illustrate an average concentration profile inside a cluster even in the case of including various sizes of ellipsoidal clusters. The profile can be with respect to absolute distance and includes errors in each data point. The reliability of the developed code was verified by analyzing an artificial cluster model which has inhomogeneous elemental distribution. It was found that the precise estimation of cluster centroids is important and that the preferable conditions for targeting clusters are a detection efficiency of over 20%, over 30 atoms in a cluster on average, and over 100 atoms for each concentration data point.

Keywords

atom probe tomographyclusterdata analysiselemental concentration distribution
Type
Data Analysis
Information
Microscopy and Microanalysis , Volume 25 , Special Issue 2: Atom Probe Tomography and Microscopy APT&M 2018 , April 2019 , pp. 349 - 355
Copyright
Copyright © Microscopy Society of America 2019 

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

References

Aruga, Y, Kozuka, M, Takaki, Y & Sato, T (2015). Formation and reversion of clusters during natural aging and subsequent artificial aging in an Al–Mg–Si alloy. Mat Sci Eng A631, 86–96.Google Scholar
Aruga, Y, Kozuka, M, Takaki, Y & Sato, T (2016). Effects of natural aging after pre-aging on clustering and bake-hardening behavior in an Al–Mg–Si alloy. Script Mat 116, 82–86.Google Scholar
Cairney, JM, Rajan, K, Haley, D, Gault, B, Bagot, PAJ, Choi, P, Felfer, PJ, Ringer, SP, Marceau, RKW & Moody, MP (2015). Mining information from atom probe data. Ultramicroscopy 159, 324–337.Google Scholar
Ceguerra, AV, Moody, MP, Powles, RC, Petersen, TC, Marceau, RKW & Ringer, SP (2012). Short-range order in multicomponent materials. Acta Cryst A68, 547–560.Google Scholar
Gault, B, Moody, MP, Cairney, JM & Ringer, SP (2012). Atom Probe Microscopy. New York, US: Springer.Google Scholar
Hyde, JM & English, CA (2000). An analysis of the structure of irradiation induced Cu-enriched clusters in low and high nickel welds. Mat Res Soc Symp Proc 650, R6.6.1–R6.6.12.Google Scholar
Marceau, RKW, Sha, G, Ferragut, R, Dupasquier, A & Ringer, SP (2010). Solute clustering in Al–Cu–Mg alloys during the early stages of elevated temperature ageing. Acta Mat 58, 4923–4939.Google Scholar
Marceau, RKW, Stephenson, LT, Hutchinson, CR & Ringer, SP (2011). Quantitative atom probe analysis of nanostructure containing clusters and precipitates with multiple length scales. Ultramicroscpy 111, 738–742.Google Scholar
Matsuda, K, Gamada, H, Fujii, K, Yoshida, T, Sato, T, Kamio, A & Ikeno, S (1997). HRTEM observation of metastable phase in Al-Mg2Si alloys. J JILM 47, 493–499.Google Scholar
Miller, MK (2000). Atom Probe Tomography. New York, US: Springer.Google Scholar
Pogatscher, S, Antrekowitsch, H, Leitner, H, Ebner, T & Uggowitzer, PJ (2011). Mechanisms controlling the artificial aging of Al–Mg–Si alloys. Acta Mat 59, 3352–3363.Google Scholar
Stephenson, LT, Moody, MP, Gault, B & Ringer, SP (2011). Estimating the physical cluster-size distribution within materials using atom-probe. Microsc Res Tech 74, 799–803.Google Scholar
Stephenson, LT, Moody, MP, Liddicoat, PV & Ringer, SP (2007). New techniques for the analysis of fine-scaled clustering phenomena within atom probe tomography (APT) data. Microsc Microanal 13, 448–463.Google Scholar
Vurpillot, F, Bostel, A & Blavette, D (2000). Trajectory overlaps and local magnification in three-dimensional atom probe. Appl Phys Lett 76, 3127–3139.Google Scholar
Vurpillot, F & Oberdorfer, C (2015). Modeling atom probe tomography: A review. Ultramicroscopy 159, 202–216.Google Scholar
Weng, Y, Jia, Z, Ding, L, Pan, Y, Liu, Y & Liu, Q (2017). Effect of Ag and Cu additions on natural aging and precipitation hardening behavior in Al-Mg-Si alloys. J Alloys Comp 695, 2444–2452.Google Scholar
Wilm, A (1911). Physical metallurgical experiments on aluminum alloys containing magnesium. Metallurgie 8, 223.Google Scholar