Additive Manufacturing Powder Particle Size Distributions: Comparison of Histogram Binning Methods

Courtney Gallagher; Emmett Kerr; Shaun McFadden

doi:10.3390/engproc2024065014

Additive Manufacturing Powder Particle Size Distributions: Comparison of Histogram Binning Methods

Courtney Gallagher
, Emmett Kerr
, Shaun McFadden

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

1 Citation (Scopus)

68 Downloads (Pure)

Abstract

Additive manufacturing powders require a well-defined Particle Size Distribution (PSD) and spherical morphology to ensure good flowability. To simplify characterisation, powders can be prepared using standard metallurgical techniques followed by optical imaging of the cross-sectioned particles. Measured PSDs of particle sections are typically underestimates of the true PSD; hence, stereological corrections are required. Variations arise in the histogram binning methods (central binning versus upper limit binning) of commonly used stereological corrections. Although the results show some sensitivity to the binning method used, the GCO method seemed reasonably robust to changes in the binning method. However, authors are encouraged to follow the method as it is intended within the literature, which was found to be especially true when using Saltykov's method.

Original language	English
Title of host publication	The 39th International Manufacturing Conference
Subtitle of host publication	Smart Manufacturing: The Next Generation
Publisher	MDPI
Pages	33-34
Number of pages	2
Volume	65
Edition	1
DOIs	https://doi.org/10.3390/engproc2024065014
Publication status	Published online - 25 Mar 2024
Event	The 39th International Manufacturing Conference: Smart Manufacturing - The Next Generation - Ulster University, Magee Campus, Derry/Londonderry, Northern Ireland Duration: 24 Aug 2023 → 25 Aug 2023 https://www.manufacturingcouncil.ie/imc39-2023

Publication series

Name	Engineering Proceedings
Publisher	MDPI

Conference

Conference	The 39th International Manufacturing Conference
Abbreviated title	IMC39 2023
Country/Territory	Northern Ireland
City	Derry/Londonderry
Period	24/08/23 → 25/08/23
Internet address	https://www.manufacturingcouncil.ie/imc39-2023

Bibliographical note

Publisher Copyright:
© 2024 by the authors.

Funding

This research was funded by the Department for the Economy, Northern Ireland, in the form of a Postgraduate Research Studentship.

Funders
Department for the Economy (Northern Ireland)

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

SDG 9 Industry, Innovation, and Infrastructure

Keywords

Additive Manufacturing
Stereology
Particle Size Distribution

Access to Document

10.3390/engproc2024065014Licence: CC BY

IMC39 Extended AbstractAuthor Accepted Manuscript, 231 KB

Cite this

Gallagher, C., Kerr, E., & McFadden, S. (2024). Additive Manufacturing Powder Particle Size Distributions: Comparison of Histogram Binning Methods. In The 39th International Manufacturing Conference: Smart Manufacturing: The Next Generation (1 ed., Vol. 65, pp. 33-34). (Engineering Proceedings). MDPI. Advance online publication. https://doi.org/10.3390/engproc2024065014

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title = "Additive Manufacturing Powder Particle Size Distributions: Comparison of Histogram Binning Methods",

abstract = "Additive manufacturing powders require a well-defined Particle Size Distribution (PSD) and spherical morphology to ensure good flowability. To simplify characterisation, powders can be prepared using standard metallurgical techniques followed by optical imaging of the cross-sectioned particles. Measured PSDs of particle sections are typically underestimates of the true PSD; hence, stereological corrections are required. Variations arise in the histogram binning methods (central binning versus upper limit binning) of commonly used stereological corrections. Although the results show some sensitivity to the binning method used, the GCO method seemed reasonably robust to changes in the binning method. However, authors are encouraged to follow the method as it is intended within the literature, which was found to be especially true when using Saltykov's method.",

keywords = "Additive Manufacturing, Stereology, Particle Size Distribution",

author = "Courtney Gallagher and Emmett Kerr and Shaun McFadden",

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Gallagher, C, Kerr, E & McFadden, S 2024, Additive Manufacturing Powder Particle Size Distributions: Comparison of Histogram Binning Methods. in The 39th International Manufacturing Conference: Smart Manufacturing: The Next Generation. 1 edn, vol. 65, Engineering Proceedings, MDPI, pp. 33-34, The 39th International Manufacturing Conference, Derry/Londonderry, Northern Ireland, 24/08/23. https://doi.org/10.3390/engproc2024065014

Additive Manufacturing Powder Particle Size Distributions: Comparison of Histogram Binning Methods. / Gallagher, Courtney; Kerr, Emmett ; McFadden, Shaun.
The 39th International Manufacturing Conference: Smart Manufacturing: The Next Generation. Vol. 65 1. ed. MDPI, 2024. p. 33-34 (Engineering Proceedings).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

TY - GEN

T1 - Additive Manufacturing Powder Particle Size Distributions: Comparison of Histogram Binning Methods

AU - Gallagher, Courtney

AU - Kerr, Emmett

AU - McFadden, Shaun

PY - 2024/3/25

Y1 - 2024/3/25

N2 - Additive manufacturing powders require a well-defined Particle Size Distribution (PSD) and spherical morphology to ensure good flowability. To simplify characterisation, powders can be prepared using standard metallurgical techniques followed by optical imaging of the cross-sectioned particles. Measured PSDs of particle sections are typically underestimates of the true PSD; hence, stereological corrections are required. Variations arise in the histogram binning methods (central binning versus upper limit binning) of commonly used stereological corrections. Although the results show some sensitivity to the binning method used, the GCO method seemed reasonably robust to changes in the binning method. However, authors are encouraged to follow the method as it is intended within the literature, which was found to be especially true when using Saltykov's method.

AB - Additive manufacturing powders require a well-defined Particle Size Distribution (PSD) and spherical morphology to ensure good flowability. To simplify characterisation, powders can be prepared using standard metallurgical techniques followed by optical imaging of the cross-sectioned particles. Measured PSDs of particle sections are typically underestimates of the true PSD; hence, stereological corrections are required. Variations arise in the histogram binning methods (central binning versus upper limit binning) of commonly used stereological corrections. Although the results show some sensitivity to the binning method used, the GCO method seemed reasonably robust to changes in the binning method. However, authors are encouraged to follow the method as it is intended within the literature, which was found to be especially true when using Saltykov's method.

KW - Additive Manufacturing

KW - Stereology

KW - Particle Size Distribution

UR - https://www.scopus.com/pages/publications/85197302042

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M3 - Conference contribution

VL - 65

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BT - The 39th International Manufacturing Conference

PB - MDPI

T2 - The 39th International Manufacturing Conference

Y2 - 24 August 2023 through 25 August 2023

ER -

Additive Manufacturing Powder Particle Size Distributions: Comparison of Histogram Binning Methods

Abstract

Publication series

Conference

Bibliographical note

Funding

UN SDGs

Keywords

Access to Document

Other files and links

Fingerprint

Morphology characterisation of additive manufacturing powder through automated image analysis algorithms

Cite this

Additive Manufacturing Powder Particle Size Distributions: Comparison of Histogram Binning Methods

Abstract

Publication series

Conference

Bibliographical note

Funding

UN SDGs

Keywords

Access to Document

Other files and links

Fingerprint

Student theses

Morphology characterisation of additive manufacturing powder through automated image analysis algorithms

Cite this