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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...
Autores principales: | , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Nature Publishing Group UK
2019
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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. |
format | Online Article Text |
id | pubmed-6803717 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2019 |
publisher | Nature Publishing Group UK |
record_format | MEDLINE/PubMed |
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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