Cargando…

Automatic Method for Vickers Hardness Estimation by Image Processing

Hardness is one of the most important mechanical properties of materials, since it is used to estimate their quality and to determine their suitability for a particular application. One method of determining quality is the Vickers hardness test, in which the resistance to plastic deformation at the...

Descripción completa

Detalles Bibliográficos
Autores principales: Polanco, Jonatan D., Jacanamejoy-Jamioy, Carlos, Mambuscay, Claudia L., Piamba, Jeferson F., Forero, Manuel G.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9863246/
https://www.ncbi.nlm.nih.gov/pubmed/36662106
http://dx.doi.org/10.3390/jimaging9010008
_version_ 1784875287534632960
author Polanco, Jonatan D.
Jacanamejoy-Jamioy, Carlos
Mambuscay, Claudia L.
Piamba, Jeferson F.
Forero, Manuel G.
author_facet Polanco, Jonatan D.
Jacanamejoy-Jamioy, Carlos
Mambuscay, Claudia L.
Piamba, Jeferson F.
Forero, Manuel G.
author_sort Polanco, Jonatan D.
collection PubMed
description Hardness is one of the most important mechanical properties of materials, since it is used to estimate their quality and to determine their suitability for a particular application. One method of determining quality is the Vickers hardness test, in which the resistance to plastic deformation at the surface of the material is measured after applying force with an indenter. The hardness is measured from the sample image, which is a tedious, time-consuming, and prone to human error procedure. Therefore, in this work, a new automatic method based on image processing techniques is proposed, allowing for obtaining results quickly and more accurately even with high irregularities in the indentation mark. For the development and validation of the method, a set of microscopy images of samples indented with applied forces of [Formula: see text] and [Formula: see text] on AISI D2 steel with and without quenching, tempering heat treatment and samples coated with titanium niobium nitride (TiNbN) was used. The proposed method was implemented as a plugin of the ImageJ program, allowing for obtaining reproducible Vickers hardness results in an average time of [Formula: see text] seconds with an accuracy of [Formula: see text] and a maximum error of [Formula: see text] with respect to the values obtained manually, used as a golden standard.
format Online
Article
Text
id pubmed-9863246
institution National Center for Biotechnology Information
language English
publishDate 2022
publisher MDPI
record_format MEDLINE/PubMed
spelling pubmed-98632462023-01-22 Automatic Method for Vickers Hardness Estimation by Image Processing Polanco, Jonatan D. Jacanamejoy-Jamioy, Carlos Mambuscay, Claudia L. Piamba, Jeferson F. Forero, Manuel G. J Imaging Article Hardness is one of the most important mechanical properties of materials, since it is used to estimate their quality and to determine their suitability for a particular application. One method of determining quality is the Vickers hardness test, in which the resistance to plastic deformation at the surface of the material is measured after applying force with an indenter. The hardness is measured from the sample image, which is a tedious, time-consuming, and prone to human error procedure. Therefore, in this work, a new automatic method based on image processing techniques is proposed, allowing for obtaining results quickly and more accurately even with high irregularities in the indentation mark. For the development and validation of the method, a set of microscopy images of samples indented with applied forces of [Formula: see text] and [Formula: see text] on AISI D2 steel with and without quenching, tempering heat treatment and samples coated with titanium niobium nitride (TiNbN) was used. The proposed method was implemented as a plugin of the ImageJ program, allowing for obtaining reproducible Vickers hardness results in an average time of [Formula: see text] seconds with an accuracy of [Formula: see text] and a maximum error of [Formula: see text] with respect to the values obtained manually, used as a golden standard. MDPI 2022-12-30 /pmc/articles/PMC9863246/ /pubmed/36662106 http://dx.doi.org/10.3390/jimaging9010008 Text en © 2022 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
Polanco, Jonatan D.
Jacanamejoy-Jamioy, Carlos
Mambuscay, Claudia L.
Piamba, Jeferson F.
Forero, Manuel G.
Automatic Method for Vickers Hardness Estimation by Image Processing
title Automatic Method for Vickers Hardness Estimation by Image Processing
title_full Automatic Method for Vickers Hardness Estimation by Image Processing
title_fullStr Automatic Method for Vickers Hardness Estimation by Image Processing
title_full_unstemmed Automatic Method for Vickers Hardness Estimation by Image Processing
title_short Automatic Method for Vickers Hardness Estimation by Image Processing
title_sort automatic method for vickers hardness estimation by image processing
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9863246/
https://www.ncbi.nlm.nih.gov/pubmed/36662106
http://dx.doi.org/10.3390/jimaging9010008
work_keys_str_mv AT polancojonatand automaticmethodforvickershardnessestimationbyimageprocessing
AT jacanamejoyjamioycarlos automaticmethodforvickershardnessestimationbyimageprocessing
AT mambuscayclaudial automaticmethodforvickershardnessestimationbyimageprocessing
AT piambajefersonf automaticmethodforvickershardnessestimationbyimageprocessing
AT foreromanuelg automaticmethodforvickershardnessestimationbyimageprocessing