Please use this identifier to cite or link to this item: https://doi.org/10.1016/j.matdes.2021.110125
Title: Machine learning for 3D printed multi-materials tissue-mimicking anatomical models
Authors: Goh, Guo Dong
Sing, Swee Leong 
Lim, Yuan Fang
Thong, Jia Li Janessa
Peh, Zhen Kai
Mogali, Sreenivasulu Reddy
Yeong, Wai Yee
Keywords: Additive manufacturing
Anatomical model
Machine learning
Multi-material
Tissue-mimic
Issue Date: 1-Dec-2021
Publisher: Elsevier Ltd
Citation: Goh, Guo Dong, Sing, Swee Leong, Lim, Yuan Fang, Thong, Jia Li Janessa, Peh, Zhen Kai, Mogali, Sreenivasulu Reddy, Yeong, Wai Yee (2021-12-01). Machine learning for 3D printed multi-materials tissue-mimicking anatomical models. Materials and Design 211 : 110125. ScholarBank@NUS Repository. https://doi.org/10.1016/j.matdes.2021.110125
Rights: Attribution 4.0 International
Abstract: 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. © 2021 The Author(s)
Source Title: Materials and Design
URI: https://scholarbank.nus.edu.sg/handle/10635/233546
ISSN: 0264-1275
DOI: 10.1016/j.matdes.2021.110125
Rights: Attribution 4.0 International
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