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Intelligent modeling and optimization of titanium surface etching for dental implant application
Acid-etching is one of the most popular processes for the surface treatment of dental implants. In this paper, acid-etching of commercially pure titanium (cpTi) in a 48% H(2)SO(4) solution is investigated. The etching process time (0–8 h) and solution temperature (25–90 °C) are assumed to be the mos...
Autores principales: | , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
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Nature Publishing Group UK
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9065129/ https://www.ncbi.nlm.nih.gov/pubmed/35504969 http://dx.doi.org/10.1038/s41598-022-11254-0 |
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author | Sadati Tilebon, Seyyed Mohamad Emamian, Seyed Amirhossein Ramezanpour, Hosseinali Yousefi, Hashem Özcan, Mutlu Naghib, Seyed Morteza Zare, Yasser Rhee, Kyong Yop |
author_facet | Sadati Tilebon, Seyyed Mohamad Emamian, Seyed Amirhossein Ramezanpour, Hosseinali Yousefi, Hashem Özcan, Mutlu Naghib, Seyed Morteza Zare, Yasser Rhee, Kyong Yop |
author_sort | Sadati Tilebon, Seyyed Mohamad |
collection | PubMed |
description | Acid-etching is one of the most popular processes for the surface treatment of dental implants. In this paper, acid-etching of commercially pure titanium (cpTi) in a 48% H(2)SO(4) solution is investigated. The etching process time (0–8 h) and solution temperature (25–90 °C) are assumed to be the most effective operational conditions to affect the surface roughness parameters such as arithmetical mean deviation of the assessed profile on the surface (R(a)) and average of maximum peak to valley height of the surface over considered length profile (R(z)), as well as weight loss (WL) of the dental implants in etching process. For the first time, three multilayer perceptron artificial neural network (MLP-ANN) with two hidden layers was optimized to predict R(a), R(z), and WL. MLP is a feedforward class of ANN and ANN model that involves computations and mathematics which simulate the human–brain processes. The ANN models can properly predict R(a), R(z), and WL variations during etching as a function of process temperature and time. Moreover, WL can be increased to achieve a high Ra. At WL = 0, R(a) of 0.5 μm is obtained, whereas R(a) increases to 2 μm at WL = 0.78 μg/cm(2). Also, ANN model was fed into a nonlinear sorting genetic algorithm (NSGA-II) to establish the optimization process and the ability of this method has been proven to predict the optimized etching conditions. |
format | Online Article Text |
id | pubmed-9065129 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | Nature Publishing Group UK |
record_format | MEDLINE/PubMed |
spelling | pubmed-90651292022-05-04 Intelligent modeling and optimization of titanium surface etching for dental implant application Sadati Tilebon, Seyyed Mohamad Emamian, Seyed Amirhossein Ramezanpour, Hosseinali Yousefi, Hashem Özcan, Mutlu Naghib, Seyed Morteza Zare, Yasser Rhee, Kyong Yop Sci Rep Article Acid-etching is one of the most popular processes for the surface treatment of dental implants. In this paper, acid-etching of commercially pure titanium (cpTi) in a 48% H(2)SO(4) solution is investigated. The etching process time (0–8 h) and solution temperature (25–90 °C) are assumed to be the most effective operational conditions to affect the surface roughness parameters such as arithmetical mean deviation of the assessed profile on the surface (R(a)) and average of maximum peak to valley height of the surface over considered length profile (R(z)), as well as weight loss (WL) of the dental implants in etching process. For the first time, three multilayer perceptron artificial neural network (MLP-ANN) with two hidden layers was optimized to predict R(a), R(z), and WL. MLP is a feedforward class of ANN and ANN model that involves computations and mathematics which simulate the human–brain processes. The ANN models can properly predict R(a), R(z), and WL variations during etching as a function of process temperature and time. Moreover, WL can be increased to achieve a high Ra. At WL = 0, R(a) of 0.5 μm is obtained, whereas R(a) increases to 2 μm at WL = 0.78 μg/cm(2). Also, ANN model was fed into a nonlinear sorting genetic algorithm (NSGA-II) to establish the optimization process and the ability of this method has been proven to predict the optimized etching conditions. Nature Publishing Group UK 2022-05-03 /pmc/articles/PMC9065129/ /pubmed/35504969 http://dx.doi.org/10.1038/s41598-022-11254-0 Text en © The Author(s) 2022 https://creativecommons.org/licenses/by/4.0/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 licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence 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 licence, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) . |
spellingShingle | Article Sadati Tilebon, Seyyed Mohamad Emamian, Seyed Amirhossein Ramezanpour, Hosseinali Yousefi, Hashem Özcan, Mutlu Naghib, Seyed Morteza Zare, Yasser Rhee, Kyong Yop Intelligent modeling and optimization of titanium surface etching for dental implant application |
title | Intelligent modeling and optimization of titanium surface etching for dental implant application |
title_full | Intelligent modeling and optimization of titanium surface etching for dental implant application |
title_fullStr | Intelligent modeling and optimization of titanium surface etching for dental implant application |
title_full_unstemmed | Intelligent modeling and optimization of titanium surface etching for dental implant application |
title_short | Intelligent modeling and optimization of titanium surface etching for dental implant application |
title_sort | intelligent modeling and optimization of titanium surface etching for dental implant application |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9065129/ https://www.ncbi.nlm.nih.gov/pubmed/35504969 http://dx.doi.org/10.1038/s41598-022-11254-0 |
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