An Optimization Method for Additive-Manufactured Implants and the Prediction of Peri-implant Osseointegration by Using Bone Density Distribution and Cell Differentiation

碩士 === 國立交通大學 === 材料科學與工程學系所 === 106 === The geometry of healing chamber in implants affects the biological healing pattern. In the current study, a method to optimize the shape of healing chamber in additive-manufactured implants for better osseointegration is proposed. It adopts the modified bone...

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Main Authors: Chien, Shih-Hsun, 簡士勛
Other Authors: Nien, Ti-Tsou
Format: Others
Language:zh-TW
Published: 2018
Online Access:http://ndltd.ncl.edu.tw/handle/2eda5d
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spelling ndltd-TW-106NCTU51590752019-09-26T03:28:11Z http://ndltd.ncl.edu.tw/handle/2eda5d An Optimization Method for Additive-Manufactured Implants and the Prediction of Peri-implant Osseointegration by Using Bone Density Distribution and Cell Differentiation 積層製造植體最佳化設計並藉由骨密度分布及細胞分化模型預測周圍骨整合之情形 Chien, Shih-Hsun 簡士勛 碩士 國立交通大學 材料科學與工程學系所 106 The geometry of healing chamber in implants affects the biological healing pattern. In the current study, a method to optimize the shape of healing chamber in additive-manufactured implants for better osseointegration is proposed. It adopts the modified bone remodeling algorithm and is implemented by finite element software ANSYS. The concept is that the disused bone elements located in the healing chambers, which are expected to be less likely to survive, will be replaced by implant elements. Similarly, the implant elements adjacent to the bone elements which attempt to grow, will be replaced by bone elements. The objective of the procedure is to maximize the fraction of the healthy surrounding bone and bone-implant-contact area. It allows an automatic formation of a complicated configuration of the healing chamber. Based on the calculation, the optimized implant gives the volume fraction of the healthy surrounding bone about 13% and enhances the bone-implant-contact area also about 41%, compared with it of the commercial ITI implant. After the optimization procedure. The current study proposes two algorithms, bone density distribution and cell differentiation model, to verify our design method. The algorithms both are implemented by using finite element package ANSYS and predict the adaptive bone-remodeling, primary stability, and osseointegration around the dental implants under loads. The bone density distribution model can pridict the long term balanced nonhomogeneous distribution state of the bone density/elastic modulus; the cell differentiation model can predict the osseointegration level between the considered implants and bones after surgical treatment. The results show that the the optimization dental implant has better osseointegration performance, compared with the commercial one. Nien, Ti-Tsou 鄒年棣 2018 學位論文 ; thesis 76 zh-TW
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language zh-TW
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sources NDLTD
description 碩士 === 國立交通大學 === 材料科學與工程學系所 === 106 === The geometry of healing chamber in implants affects the biological healing pattern. In the current study, a method to optimize the shape of healing chamber in additive-manufactured implants for better osseointegration is proposed. It adopts the modified bone remodeling algorithm and is implemented by finite element software ANSYS. The concept is that the disused bone elements located in the healing chambers, which are expected to be less likely to survive, will be replaced by implant elements. Similarly, the implant elements adjacent to the bone elements which attempt to grow, will be replaced by bone elements. The objective of the procedure is to maximize the fraction of the healthy surrounding bone and bone-implant-contact area. It allows an automatic formation of a complicated configuration of the healing chamber. Based on the calculation, the optimized implant gives the volume fraction of the healthy surrounding bone about 13% and enhances the bone-implant-contact area also about 41%, compared with it of the commercial ITI implant. After the optimization procedure. The current study proposes two algorithms, bone density distribution and cell differentiation model, to verify our design method. The algorithms both are implemented by using finite element package ANSYS and predict the adaptive bone-remodeling, primary stability, and osseointegration around the dental implants under loads. The bone density distribution model can pridict the long term balanced nonhomogeneous distribution state of the bone density/elastic modulus; the cell differentiation model can predict the osseointegration level between the considered implants and bones after surgical treatment. The results show that the the optimization dental implant has better osseointegration performance, compared with the commercial one.
author2 Nien, Ti-Tsou
author_facet Nien, Ti-Tsou
Chien, Shih-Hsun
簡士勛
author Chien, Shih-Hsun
簡士勛
spellingShingle Chien, Shih-Hsun
簡士勛
An Optimization Method for Additive-Manufactured Implants and the Prediction of Peri-implant Osseointegration by Using Bone Density Distribution and Cell Differentiation
author_sort Chien, Shih-Hsun
title An Optimization Method for Additive-Manufactured Implants and the Prediction of Peri-implant Osseointegration by Using Bone Density Distribution and Cell Differentiation
title_short An Optimization Method for Additive-Manufactured Implants and the Prediction of Peri-implant Osseointegration by Using Bone Density Distribution and Cell Differentiation
title_full An Optimization Method for Additive-Manufactured Implants and the Prediction of Peri-implant Osseointegration by Using Bone Density Distribution and Cell Differentiation
title_fullStr An Optimization Method for Additive-Manufactured Implants and the Prediction of Peri-implant Osseointegration by Using Bone Density Distribution and Cell Differentiation
title_full_unstemmed An Optimization Method for Additive-Manufactured Implants and the Prediction of Peri-implant Osseointegration by Using Bone Density Distribution and Cell Differentiation
title_sort optimization method for additive-manufactured implants and the prediction of peri-implant osseointegration by using bone density distribution and cell differentiation
publishDate 2018
url http://ndltd.ncl.edu.tw/handle/2eda5d
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