Mitigating Scattering Effects in Light-Based Three-Dimensional Printing Using Machine LearningSource: Journal of Manufacturing Science and Engineering:;2020:;volume( 142 ):;issue: 008Author:You, Shangting
,
Guan, Jiaao
,
Alido, Jeffrey
,
Hwang, Henry H.
,
Yu, Ronald
,
Kwe, Leilani
,
Su, Hao
,
Chen, Shaochen
DOI: 10.1115/1.4046986Publisher: The American Society of Mechanical Engineers (ASME)
Abstract: When using light-based three-dimensional (3D) printing methods to fabricate functional micro-devices, unwanted light scattering during the printing process is a significant challenge to achieve high-resolution fabrication. We report the use of a deep neural network (NN)-based machine learning (ML) technique to mitigate the scattering effect, where our NN was employed to study the highly sophisticated relationship between the input digital masks and their corresponding output 3D printed structures. Furthermore, the NN was used to model an inverse 3D printing process, where it took desired printed structures as inputs and subsequently generated grayscale digital masks that optimized the light exposure dose according to the desired structures’ local features. Verification results showed that using NN-generated digital masks yielded significant improvements in printing fidelity when compared with using masks identical to the desired structures.
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contributor author | You, Shangting | |
contributor author | Guan, Jiaao | |
contributor author | Alido, Jeffrey | |
contributor author | Hwang, Henry H. | |
contributor author | Yu, Ronald | |
contributor author | Kwe, Leilani | |
contributor author | Su, Hao | |
contributor author | Chen, Shaochen | |
date accessioned | 2022-02-04T14:18:21Z | |
date available | 2022-02-04T14:18:21Z | |
date copyright | 2020/05/14/ | |
date issued | 2020 | |
identifier issn | 1087-1357 | |
identifier other | manu_142_8_081002.pdf | |
identifier uri | http://yetl.yabesh.ir/yetl1/handle/yetl/4273389 | |
description abstract | When using light-based three-dimensional (3D) printing methods to fabricate functional micro-devices, unwanted light scattering during the printing process is a significant challenge to achieve high-resolution fabrication. We report the use of a deep neural network (NN)-based machine learning (ML) technique to mitigate the scattering effect, where our NN was employed to study the highly sophisticated relationship between the input digital masks and their corresponding output 3D printed structures. Furthermore, the NN was used to model an inverse 3D printing process, where it took desired printed structures as inputs and subsequently generated grayscale digital masks that optimized the light exposure dose according to the desired structures’ local features. Verification results showed that using NN-generated digital masks yielded significant improvements in printing fidelity when compared with using masks identical to the desired structures. | |
publisher | The American Society of Mechanical Engineers (ASME) | |
title | Mitigating Scattering Effects in Light-Based Three-Dimensional Printing Using Machine Learning | |
type | Journal Paper | |
journal volume | 142 | |
journal issue | 8 | |
journal title | Journal of Manufacturing Science and Engineering | |
identifier doi | 10.1115/1.4046986 | |
page | 81002 | |
tree | Journal of Manufacturing Science and Engineering:;2020:;volume( 142 ):;issue: 008 | |
contenttype | Fulltext |