High-speed Nanoindentation Data Analysis of WC-based Cemented Carbides using Gaussian Mixture Model Clustering and Skew-normal Mixture: Beyond Gaussian Deconvolution

The mapping of micromechanical properties of heterogeneous materials with high-speed nanoindentation faces challenges in data interpretation and extraction of reliable phase properties. Gaussian deconvolution has commonly been used to treat this data, but problems arise when microstructures are fine...

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Autores: Ortiz-Membrado, L, Benítez, R, Llanes, L, Jiménez-Piqué, E
Tipo de recurso: artículo
Estado:Versión publicada
Fecha de publicación:2025
País:España
Institución:Fundació Sant Joan de Déu
Repositorio:r-FSJD. Repositorio Institucional de Producción Científica de la Fundació Sant Joan de Déu
OAI Identifier:oai:fsjd.fundanetsuite.com:p28421
Acceso en línea:https://fsjd.fundanetsuite.com/Publicaciones/ProdCientif/PublicacionFrw.aspx?id=28421
Access Level:acceso abierto
Palabra clave:WC hardmetals
High-speed nanoindentation
Mechanical properties
Statistical analysis
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spelling High-speed Nanoindentation Data Analysis of WC-based Cemented Carbides using Gaussian Mixture Model Clustering and Skew-normal Mixture: Beyond Gaussian DeconvolutionOrtiz-Membrado, LBenítez, RLlanes, LJiménez-Piqué, EWC hardmetalsHigh-speed nanoindentationMechanical propertiesStatistical analysisThe mapping of micromechanical properties of heterogeneous materials with high-speed nanoindentation faces challenges in data interpretation and extraction of reliable phase properties. Gaussian deconvolution has commonly been used to treat this data, but problems arise when microstructures are fine compared to the indentation imprint size and the mechanical properties of the different phases are not extremely different. Recently machine-learning methods, such as clustering techniques, have emerged as good approaches to assess these challenges. Within this context, it is important to understand the micromechanical properties of cemented carbides, materials usually referred to as hardmetals, but their complex microstructure poses challenges for assessment and interpretation. The study reveals insights from high-speed nanoindentation data treatment on hardmetals with different microstructures and binder compostion. The data has been statistically analyzed by means of a clustering method: Gaussian Mixture Model (GMM) and the fitting of a mixture of skew-normal distributions. Findings underscore the asymmetry in phase properties, and the challenges GMM encounters in some samples. The skew-normal method offers enhanced precision and addresses issues related to scatter in phase intersections, providing a more accurate representation of fine microstructural features. The combined approach of GMM and skew-normal proves consistent for reliable evaluation of micromechanical properties from nanoindentation maps in cemented carbides, and demonstrate the potential of this technique to be applied to novel hardmetal compositions as well as other composites.ELSEVIER SCI LTD2025info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionhttps://fsjd.fundanetsuite.com/Publicaciones/ProdCientif/PublicacionFrw.aspx?id=28421INTERNATIONAL JOURNAL OF REFRACTORY METALS & HARD MATERIALSISSN: 02634368ISSNe: 22133917reponame:r-FSJD. Repositorio Institucional de Producción Científica de la Fundació Sant Joan de Déuinstname:Fundació Sant Joan de DéuInglésinfo:eu-repo/semantics/openAccessoai:fsjd.fundanetsuite.com:p284212026-05-27T12:37:41Z
dc.title.none.fl_str_mv High-speed Nanoindentation Data Analysis of WC-based Cemented Carbides using Gaussian Mixture Model Clustering and Skew-normal Mixture: Beyond Gaussian Deconvolution
title High-speed Nanoindentation Data Analysis of WC-based Cemented Carbides using Gaussian Mixture Model Clustering and Skew-normal Mixture: Beyond Gaussian Deconvolution
spellingShingle High-speed Nanoindentation Data Analysis of WC-based Cemented Carbides using Gaussian Mixture Model Clustering and Skew-normal Mixture: Beyond Gaussian Deconvolution
Ortiz-Membrado, L
WC hardmetals
High-speed nanoindentation
Mechanical properties
Statistical analysis
title_short High-speed Nanoindentation Data Analysis of WC-based Cemented Carbides using Gaussian Mixture Model Clustering and Skew-normal Mixture: Beyond Gaussian Deconvolution
title_full High-speed Nanoindentation Data Analysis of WC-based Cemented Carbides using Gaussian Mixture Model Clustering and Skew-normal Mixture: Beyond Gaussian Deconvolution
title_fullStr High-speed Nanoindentation Data Analysis of WC-based Cemented Carbides using Gaussian Mixture Model Clustering and Skew-normal Mixture: Beyond Gaussian Deconvolution
title_full_unstemmed High-speed Nanoindentation Data Analysis of WC-based Cemented Carbides using Gaussian Mixture Model Clustering and Skew-normal Mixture: Beyond Gaussian Deconvolution
title_sort High-speed Nanoindentation Data Analysis of WC-based Cemented Carbides using Gaussian Mixture Model Clustering and Skew-normal Mixture: Beyond Gaussian Deconvolution
dc.creator.none.fl_str_mv Ortiz-Membrado, L
Benítez, R
Llanes, L
Jiménez-Piqué, E
author Ortiz-Membrado, L
author_facet Ortiz-Membrado, L
Benítez, R
Llanes, L
Jiménez-Piqué, E
author_role author
author2 Benítez, R
Llanes, L
Jiménez-Piqué, E
author2_role author
author
author
dc.subject.none.fl_str_mv WC hardmetals
High-speed nanoindentation
Mechanical properties
Statistical analysis
topic WC hardmetals
High-speed nanoindentation
Mechanical properties
Statistical analysis
description The mapping of micromechanical properties of heterogeneous materials with high-speed nanoindentation faces challenges in data interpretation and extraction of reliable phase properties. Gaussian deconvolution has commonly been used to treat this data, but problems arise when microstructures are fine compared to the indentation imprint size and the mechanical properties of the different phases are not extremely different. Recently machine-learning methods, such as clustering techniques, have emerged as good approaches to assess these challenges. Within this context, it is important to understand the micromechanical properties of cemented carbides, materials usually referred to as hardmetals, but their complex microstructure poses challenges for assessment and interpretation. The study reveals insights from high-speed nanoindentation data treatment on hardmetals with different microstructures and binder compostion. The data has been statistically analyzed by means of a clustering method: Gaussian Mixture Model (GMM) and the fitting of a mixture of skew-normal distributions. Findings underscore the asymmetry in phase properties, and the challenges GMM encounters in some samples. The skew-normal method offers enhanced precision and addresses issues related to scatter in phase intersections, providing a more accurate representation of fine microstructural features. The combined approach of GMM and skew-normal proves consistent for reliable evaluation of micromechanical properties from nanoindentation maps in cemented carbides, and demonstrate the potential of this technique to be applied to novel hardmetal compositions as well as other composites.
publishDate 2025
dc.date.none.fl_str_mv 2025
dc.type.none.fl_str_mv info:eu-repo/semantics/article
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dc.identifier.none.fl_str_mv https://fsjd.fundanetsuite.com/Publicaciones/ProdCientif/PublicacionFrw.aspx?id=28421
url https://fsjd.fundanetsuite.com/Publicaciones/ProdCientif/PublicacionFrw.aspx?id=28421
dc.language.none.fl_str_mv Inglés
language_invalid_str_mv Inglés
dc.rights.none.fl_str_mv info:eu-repo/semantics/openAccess
eu_rights_str_mv openAccess
dc.publisher.none.fl_str_mv ELSEVIER SCI LTD
publisher.none.fl_str_mv ELSEVIER SCI LTD
dc.source.none.fl_str_mv INTERNATIONAL JOURNAL OF REFRACTORY METALS & HARD MATERIALS
ISSN: 02634368
ISSNe: 22133917
reponame:r-FSJD. Repositorio Institucional de Producción Científica de la Fundació Sant Joan de Déu
instname:Fundació Sant Joan de Déu
instname_str Fundació Sant Joan de Déu
reponame_str r-FSJD. Repositorio Institucional de Producción Científica de la Fundació Sant Joan de Déu
collection r-FSJD. Repositorio Institucional de Producción Científica de la Fundació Sant Joan de Déu
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