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New Method for Evaluating Surface Roughness Parameters Acquired by Laser Scanning

Quality evaluation of a material’s surface is performed through roughness analysis of surface samples. Several techniques have been presented to achieve this goal, including geometrical analysis and surface roughness analysis. Geometric analysis allows a visual and subjective evaluation of roughness...

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Autores principales: Tonietto, Leandro, Gonzaga, Luiz, Veronez, Mauricio Roberto, Kazmierczak, Claudio de Souza, Arnold, Daiana Cristina Metz, Costa, Cristiano André da
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Nature Publishing Group UK 2019
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6803717/
https://www.ncbi.nlm.nih.gov/pubmed/31636338
http://dx.doi.org/10.1038/s41598-019-51545-7
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author Tonietto, Leandro
Gonzaga, Luiz
Veronez, Mauricio Roberto
Kazmierczak, Claudio de Souza
Arnold, Daiana Cristina Metz
Costa, Cristiano André da
author_facet Tonietto, Leandro
Gonzaga, Luiz
Veronez, Mauricio Roberto
Kazmierczak, Claudio de Souza
Arnold, Daiana Cristina Metz
Costa, Cristiano André da
author_sort Tonietto, Leandro
collection PubMed
description Quality evaluation of a material’s surface is performed through roughness analysis of surface samples. Several techniques have been presented to achieve this goal, including geometrical analysis and surface roughness analysis. Geometric analysis allows a visual and subjective evaluation of roughness (a qualitative assessment), whereas computation of the roughness parameters is a quantitative assessment and allows a standardized analysis of the surfaces. In civil engineering, the process is performed with mechanical profilometer equipment (2D) without adequate accuracy and laser profilometer (3D) with no consensus on how to interpret the result quantitatively. This work proposes a new method to evaluate surface roughness, starting from the generation of a visual surface roughness signature, which is calculated through the roughness parameters computed in hierarchically organized regions. The evaluation tools presented in this new method provide a local and more accurate evaluation of the computed coefficients. In the tests performed it was possible to quantitatively analyze roughness differences between ceramic blocks and to find that a quantitative microscale analysis allows to identify the largest variation of roughness parameters R(a)avg, R(a)sdv, R(a)min and R(a)max between samples, which benefit the evaluation and comparison of the sampled surfaces.
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spelling pubmed-68037172019-10-24 New Method for Evaluating Surface Roughness Parameters Acquired by Laser Scanning Tonietto, Leandro Gonzaga, Luiz Veronez, Mauricio Roberto Kazmierczak, Claudio de Souza Arnold, Daiana Cristina Metz Costa, Cristiano André da Sci Rep Article Quality evaluation of a material’s surface is performed through roughness analysis of surface samples. Several techniques have been presented to achieve this goal, including geometrical analysis and surface roughness analysis. Geometric analysis allows a visual and subjective evaluation of roughness (a qualitative assessment), whereas computation of the roughness parameters is a quantitative assessment and allows a standardized analysis of the surfaces. In civil engineering, the process is performed with mechanical profilometer equipment (2D) without adequate accuracy and laser profilometer (3D) with no consensus on how to interpret the result quantitatively. This work proposes a new method to evaluate surface roughness, starting from the generation of a visual surface roughness signature, which is calculated through the roughness parameters computed in hierarchically organized regions. The evaluation tools presented in this new method provide a local and more accurate evaluation of the computed coefficients. In the tests performed it was possible to quantitatively analyze roughness differences between ceramic blocks and to find that a quantitative microscale analysis allows to identify the largest variation of roughness parameters R(a)avg, R(a)sdv, R(a)min and R(a)max between samples, which benefit the evaluation and comparison of the sampled surfaces. Nature Publishing Group UK 2019-10-21 /pmc/articles/PMC6803717/ /pubmed/31636338 http://dx.doi.org/10.1038/s41598-019-51545-7 Text en © The Author(s) 2019 Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/.
spellingShingle Article
Tonietto, Leandro
Gonzaga, Luiz
Veronez, Mauricio Roberto
Kazmierczak, Claudio de Souza
Arnold, Daiana Cristina Metz
Costa, Cristiano André da
New Method for Evaluating Surface Roughness Parameters Acquired by Laser Scanning
title New Method for Evaluating Surface Roughness Parameters Acquired by Laser Scanning
title_full New Method for Evaluating Surface Roughness Parameters Acquired by Laser Scanning
title_fullStr New Method for Evaluating Surface Roughness Parameters Acquired by Laser Scanning
title_full_unstemmed New Method for Evaluating Surface Roughness Parameters Acquired by Laser Scanning
title_short New Method for Evaluating Surface Roughness Parameters Acquired by Laser Scanning
title_sort new method for evaluating surface roughness parameters acquired by laser scanning
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6803717/
https://www.ncbi.nlm.nih.gov/pubmed/31636338
http://dx.doi.org/10.1038/s41598-019-51545-7
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