Machine learning for 3D printed multi-materials tissue-mimicking anatomical models
Polyjet, a material jetting 3D printing technique, has been widely used for the fabrication of patient-specific anatomical models owing to the toolless fabrication technique and its ability to print multiple materials in a single part. Although the fabrication of anatomical models with high dimensio...
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sg-ntu-dr.10356-1616602022-09-17T23:31:37Z Machine learning for 3D printed multi-materials tissue-mimicking anatomical models Goh, Guo Dong Sing, Swee Leong Lim, Yuan Fang Thong, Janessa Jia Li Peh, Zhen Kai Mogali, Sreenivasulu Reddy Yeong, Wai Yee School of Mechanical and Aerospace Engineering Lee Kong Chian School of Medicine (LKCMedicine) Singapore Centre for 3D Printing Engineering::Mechanical engineering Science::Medicine Machine Learning Anatomical Model Polyjet, a material jetting 3D printing technique, has been widely used for the fabrication of patient-specific anatomical models owing to the toolless fabrication technique and its ability to print multiple materials in a single part. Although the fabrication of anatomical models with high dimensional accuracy has been demonstrated, 3D printed anatomical models with tissue-mimicking properties have not been realized. In this study, a composite layering design was used to tune the shore hardness and compressive modulus of the Polyjet-printed parts in an attempt to mimic the properties of human tissues. 216 specimens (with 72 combinations of design parameters) were printed and tested to develop the material library for the anatomical models. An analytical model was developed to estimate the effective compressive modulus and shore hardness of the composite laminate. A neural network was used to learn the multi-dimensional relationship between the design parameters and mechanical properties. The 5-33-2 network size is found to be the optimum neural network structure with a mean square error of 0.98% for the compressive modulus, lower than the traditional response surface method model. A genetic algorithm was used to search the design space for the most optimum design parameters for the targeted effective shore hardness. Nanyang Technological University National Research Foundation (NRF) Published version This research is supported by the National Research Foundation, Prime Minister’s Office, Singapore under its Medium-Sized Centre funding scheme and the Lee Kong Chian School of Medicine, Nanyang Technological University, Singapore Start-Up Grant. 2022-09-13T07:47:46Z 2022-09-13T07:47:46Z 2021 Journal Article Goh, G. D., Sing, S. L., Lim, Y. F., Thong, J. J. L., Peh, Z. K., Mogali, S. R. & Yeong, W. Y. (2021). Machine learning for 3D printed multi-materials tissue-mimicking anatomical models. Materials and Design, 211, 110125-. https://dx.doi.org/10.1016/j.matdes.2021.110125 0261-3069 https://hdl.handle.net/10356/161660 10.1016/j.matdes.2021.110125 2-s2.0-85116860897 211 110125 en Materials and Design © 2021 The Author(s). Published by Elsevier Ltd. This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/). application/pdf |
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Engineering::Mechanical engineering Science::Medicine Machine Learning Anatomical Model Goh, Guo Dong Sing, Swee Leong Lim, Yuan Fang Thong, Janessa Jia Li Peh, Zhen Kai Mogali, Sreenivasulu Reddy Yeong, Wai Yee Machine learning for 3D printed multi-materials tissue-mimicking anatomical models |
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Polyjet, a material jetting 3D printing technique, has been widely used for the fabrication of patient-specific anatomical models owing to the toolless fabrication technique and its ability to print multiple materials in a single part. Although the fabrication of anatomical models with high dimensional accuracy has been demonstrated, 3D printed anatomical models with tissue-mimicking properties have not been realized. In this study, a composite layering design was used to tune the shore hardness and compressive modulus of the Polyjet-printed parts in an attempt to mimic the properties of human tissues. 216 specimens (with 72 combinations of design parameters) were printed and tested to develop the material library for the anatomical models. An analytical model was developed to estimate the effective compressive modulus and shore hardness of the composite laminate. A neural network was used to learn the multi-dimensional relationship between the design parameters and mechanical properties. The 5-33-2 network size is found to be the optimum neural network structure with a mean square error of 0.98% for the compressive modulus, lower than the traditional response surface method model. A genetic algorithm was used to search the design space for the most optimum design parameters for the targeted effective shore hardness. |
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School of Mechanical and Aerospace Engineering |
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School of Mechanical and Aerospace Engineering Goh, Guo Dong Sing, Swee Leong Lim, Yuan Fang Thong, Janessa Jia Li Peh, Zhen Kai Mogali, Sreenivasulu Reddy Yeong, Wai Yee |
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Article |
author |
Goh, Guo Dong Sing, Swee Leong Lim, Yuan Fang Thong, Janessa Jia Li Peh, Zhen Kai Mogali, Sreenivasulu Reddy Yeong, Wai Yee |
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Goh, Guo Dong |
title |
Machine learning for 3D printed multi-materials tissue-mimicking anatomical models |
title_short |
Machine learning for 3D printed multi-materials tissue-mimicking anatomical models |
title_full |
Machine learning for 3D printed multi-materials tissue-mimicking anatomical models |
title_fullStr |
Machine learning for 3D printed multi-materials tissue-mimicking anatomical models |
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Machine learning for 3D printed multi-materials tissue-mimicking anatomical models |
title_sort |
machine learning for 3d printed multi-materials tissue-mimicking anatomical models |
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2022 |
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https://hdl.handle.net/10356/161660 |
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