Jove
Visualize
联系我们
JoVE
x logofacebook logolinkedin logoyoutube logo
关于 JoVE
概览领导团队博客JoVE 帮助中心
作者
出版流程编辑委员会范围与政策同行评审常见问题投稿
图书馆员
用户评价订阅访问资源图书馆顾问委员会常见问题
研究
JoVE JournalMethods CollectionsJoVE Encyclopedia of Experiments存档
教育
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab Manual教师资源中心教师网站
使用条款与条件
隐私政策
政策

相关概念视频

Computed Tomography01:10

Computed Tomography

Tomography refers to imaging by sections. Computed tomography (CT) is a non-invasive imaging technique that uses computers to analyze several cross-sectional X-rays to reveal minute details about structures in the body.
The technique was invented in the 1970s and is based on the principle that as X-rays pass through the body, they are absorbed or reflected at different levels. In the technique, a patient lies on a motorized platform while a computerized axial tomography (CAT) scanner rotates...

您也可能阅读

相关文章

通过共同作者、期刊和引用图与本文相关的文章。

排序
Same author

Isomorphic contact resonance force microscopy and piezoresponse force microscopy of an AlN thin film: demonstration of a new contact resonance technique.

Nano futures·2026
Same author

Voxel-Scale Conversion Mapping Informs Intrinsic Resolution in Stereolithographic Additive Manufacturing.

ACS applied polymer materials·2026
Same author

Characterization of Electronic Stress-Induced Changes in Multilayer MoS<sub>2</sub>.

ACS applied electronic materials·2026
Same author

Extreme Size and Irradiance Dependence in High-Resolution Vat Photopolymerization of Hydrogels.

Small methods·2026
Same author

Multistage Networks for Glassy Holographic Photopolymers.

ACS applied materials & interfaces·2025
Same author

Analytical Ultracentrifugation Characterization of Differential Sedimentation Size-Separated Graphene Dispersions.

Small (Weinheim an der Bergstrasse, Germany)·2025

相关实验视频

Updated: Jun 22, 2026

Voxel Printing Anatomy: Design and Fabrication of Realistic, Presurgical Planning Models through Bitmap Printing
11:36

Voxel Printing Anatomy: Design and Fabrication of Realistic, Presurgical Planning Models through Bitmap Printing

Published on: February 9, 2022

2.8K

在灰度数字光处理增材制造中对复杂的voxel预测采用数据驱动的方法,使用U-Nets和生成对抗网络进行增材制造.

Jason P Killgore1, Thomas J Kolibaba1, Benjamin W Caplins1

  • 1Applied Chemicals and Materials Division, National Institue of Standards and Technology, Boulder, CO, 80305, USA.

Small (Weinheim an der Bergstrasse, Germany)
|July 6, 2023
PubMed
概括
此摘要是机器生成的。

机器学习模型准确地预测了数字光处理 (DLP) 增材制造中的3D打印几何. 这种以数据为导向的方法通过纠正光罩以改善voxel几何控制来提高精度.

关键词:
通过3D打印打印3D打印.数字光处理是数字光处理.机器学习是机器学习.神经网络的神经网络的神经网络像素2pixxxx 在线观看立体石版印刷是一种立体石版印刷.这就是瓦特光聚合的光聚合.

更多相关视频

Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography
04:48

Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography

Published on: November 30, 2022

2.8K
Objectification of Tongue Diagnosis in Traditional Medicine, Data Analysis, and Study Application
05:56

Objectification of Tongue Diagnosis in Traditional Medicine, Data Analysis, and Study Application

Published on: April 14, 2023

2.5K

相关实验视频

Last Updated: Jun 22, 2026

Voxel Printing Anatomy: Design and Fabrication of Realistic, Presurgical Planning Models through Bitmap Printing
11:36

Voxel Printing Anatomy: Design and Fabrication of Realistic, Presurgical Planning Models through Bitmap Printing

Published on: February 9, 2022

2.8K
Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography
04:48

Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography

Published on: November 30, 2022

2.8K
Objectification of Tongue Diagnosis in Traditional Medicine, Data Analysis, and Study Application
05:56

Objectification of Tongue Diagnosis in Traditional Medicine, Data Analysis, and Study Application

Published on: April 14, 2023

2.5K

科学领域:

  • 材料科学 材料科学 材料科学
  • 计算机科学 计算机科学

背景情况:

  • 数字光处理 (DLP) 是一种关键的增材制造技术.
  • 预测和控制voxel几何学对于DLP的精度至关重要.

研究的目的:

  • 开发和验证机器学习模型,用于在DLP中预测3D打印的voxel几何.
  • 探索U-net和pix2pix条件生成对抗网络 (cGAN) 的应用,以提高精度.

主要方法:

  • 利用共聚焦显微镜工作流程来获得高通量数据,以获取voxel交互的数据.
  • 训练有素的pix2pix cGAN模型在随机灰度尺度数字照片面膜的数据上.
  • 与实际3D打印相比,验证了模型预测,评估了子像素分辨率的准确性.

主要成果:

  • 机器学习模型展示了准确的预测3D打印的voxel几何学与子像素分辨率.
  • 经过训练的cGAN成功地进行了虚拟DLP实验,包括治疗深度和反假名.
  • pix2pix模型显示可用于比培训中使用的更大的口罩,并可以为打印故障分析提供信息.

结论:

  • 数据驱动的机器学习,特别是U-nets和cGAN,在DLP增材制造中预测和纠正光罩方面显示出重大前景.
  • 这种方法可以提高3D打印过程的精度和质量控制.