國立虎尾科技大學 |

Estimation and Utilization of Reconstruction Uncertainty for Atom Probe Feature Extraction.

Record Type:	Language materials, manuscript : Monograph/item
Title/Author:	Estimation and Utilization of Reconstruction Uncertainty for Atom Probe Feature Extraction./
Author:	Still, Evan.
Description:	1 online resource (163 pages)
Notes:	Source: Dissertations Abstracts International, Volume: 85-03, Section: B.
Contained By:	Dissertations Abstracts International85-03B.
Subject:	Nuclear engineering. -
Online resource:	click for full text (PQDT)
ISBN:	9798380382892

Estimation and Utilization of Reconstruction Uncertainty for Atom Probe Feature Extraction.
Still, Evan.

Estimation and Utilization of Reconstruction Uncertainty for Atom Probe Feature Extraction. - 1 online resource (163 pages)

Source: Dissertations Abstracts International, Volume: 85-03, Section: B.

Thesis (Ph.D.)--University of California, Berkeley, 2023.

Includes bibliographical references

Atom Probe Tomography (APT) is a powerful analytical technique for 3D characterization of materials at the atomic scale which has been widely used to study irradiation induced features. However, the accuracy and reliability of the atom probe reconstruction and post-processing methods such as cluster analysis is often neglected. In an effort to account for both of these limitations we introduce a two-step method for quantifying the quality of an atom probe reconstruction and the resulting secondary analysis.First, we developed a pointwise measure of uncertainty for APT data based on linear error propagation. This approach provide a systematic way of estimating the uncertainty in the atom positions and the most influential reconstruction parameters. The pointwise uncertainty measure can be used to assess the local quality of APT data and govern alternate reconstruction directions which minimize uncertainty. Furthermore, focusing the error analysis not on resolution but on parameter and coordinate uncertainty enables error to be propagated through complex processes such as the measurement of isotopically enriched thin films.Second, we developed a method which extends monte-carlo consensus clustering from K-based clustering algorithms to density-based clustering algorithms. In doing so a measure of relative stability is introduced to describe the ambiguity of clustering observed in an APT sample and automate the selection of the distance parameter for DBSCAN (Density-based spatial clustering of applications with noise). Our approach uses Monte-Carlo perturbation statistics, and thus could be linked to use the pointwise uncertainty established in the first part of this work, to generate alternate atom probe datasets and then apply DBSCAN to each of these datasets. In doing so the sample size for which to calculate grows in magnitude enabling more thorough post-clustering filtration methods with which to extract clusters from high-noise scenarios. We use statistical methods to analyze the results and determine the optimal DBSCAN parameters that maximize the clustering performance and minimize the uncertainty.The efficacy and utility of pointwise error propagation is demonstrated through a case study on the measurement of an isotopically enriched iron thin film while our novel clustering algorithm, Density-based Monte-Carlo Consensus Clustering (DMC3), is benchmarked against a round robin study on binary Fe-Cu systems with an emphasis on irradiation induced hardening. Our approaches provides a quantitative and objective way of assessing the quality of APT data and improving the reliability of APT data analysis.

Electronic reproduction.
Ann Arbor, Mich. :
ProQuest,
2024

Mode of access: World Wide Web

ISBN: 9798380382892Subjects--Topical Terms:

655622
Nuclear engineering.
Subjects--Index Terms:

Atom Probe TomographyIndex Terms--Genre/Form:

554714
Electronic books.

Estimation and Utilization of Reconstruction Uncertainty for Atom Probe Feature Extraction.
LDR:04090ntm a22004097 4500 001 1151818
005 20241118135831.5
006 m o d
007 cr mn ---uuuuu
008 250605s2023 xx obm 000 0 eng d
020 $a 9798380382892
035 $a (MiAaPQ)AAI30490259
035 $a AAI30490259
040 $a MiAaPQ $b eng $c MiAaPQ $d NTU
100 1 $a Still, Evan. $3 1478650
245 1 0 $a Estimation and Utilization of Reconstruction Uncertainty for Atom Probe Feature Extraction.
264 0 $c 2023
300 $a 1 online resource (163 pages)
336 $a text $b txt $2 rdacontent
337 $a computer $b c $2 rdamedia
338 $a online resource $b cr $2 rdacarrier
500 $a Source: Dissertations Abstracts International, Volume: 85-03, Section: B.
500 $a Advisor: Hosemann, Peter.
502 $a Thesis (Ph.D.)--University of California, Berkeley, 2023.
504 $a Includes bibliographical references
520 $a Atom Probe Tomography (APT) is a powerful analytical technique for 3D characterization of materials at the atomic scale which has been widely used to study irradiation induced features. However, the accuracy and reliability of the atom probe reconstruction and post-processing methods such as cluster analysis is often neglected. In an effort to account for both of these limitations we introduce a two-step method for quantifying the quality of an atom probe reconstruction and the resulting secondary analysis.First, we developed a pointwise measure of uncertainty for APT data based on linear error propagation. This approach provide a systematic way of estimating the uncertainty in the atom positions and the most influential reconstruction parameters. The pointwise uncertainty measure can be used to assess the local quality of APT data and govern alternate reconstruction directions which minimize uncertainty. Furthermore, focusing the error analysis not on resolution but on parameter and coordinate uncertainty enables error to be propagated through complex processes such as the measurement of isotopically enriched thin films.Second, we developed a method which extends monte-carlo consensus clustering from K-based clustering algorithms to density-based clustering algorithms. In doing so a measure of relative stability is introduced to describe the ambiguity of clustering observed in an APT sample and automate the selection of the distance parameter for DBSCAN (Density-based spatial clustering of applications with noise). Our approach uses Monte-Carlo perturbation statistics, and thus could be linked to use the pointwise uncertainty established in the first part of this work, to generate alternate atom probe datasets and then apply DBSCAN to each of these datasets. In doing so the sample size for which to calculate grows in magnitude enabling more thorough post-clustering filtration methods with which to extract clusters from high-noise scenarios. We use statistical methods to analyze the results and determine the optimal DBSCAN parameters that maximize the clustering performance and minimize the uncertainty.The efficacy and utility of pointwise error propagation is demonstrated through a case study on the measurement of an isotopically enriched iron thin film while our novel clustering algorithm, Density-based Monte-Carlo Consensus Clustering (DMC3), is benchmarked against a round robin study on binary Fe-Cu systems with an emphasis on irradiation induced hardening. Our approaches provides a quantitative and objective way of assessing the quality of APT data and improving the reliability of APT data analysis.
533 $a Electronic reproduction. $b Ann Arbor, Mich. : $c ProQuest, $d 2024
538 $a Mode of access: World Wide Web
650 4 $a Nuclear engineering. $3 655622
650 4 $a Materials science. $3 557839
650 4 $a Statistical physics. $3 528048
650 4 $a Computational physics. $3 1181955
653 $a Atom Probe Tomography
653 $a Clustering algorithms
653 $a Error propagation
653 $a Nuclear materials
653 $a Probe reconstruction
655 7 $a Electronic books. $2 local $3 554714
690 $a 0552
690 $a 0794
690 $a 0217
690 $a 0216
710 2 $a ProQuest Information and Learning Co. $3 1178819
710 2 $a University of California, Berkeley. $b Nuclear Engineering. $3 1181753
773 0 $t Dissertations Abstracts International $g 85-03B.
856 4 0 $u http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=30490259 $z click for full text (PQDT)