arrow
返回

Cross-printer reproducibility modeling using a Deep Auto-Encoded Multi-Domain Gaussian Process

delete2026-08-20
delete0
delete
OA
AI
L
Logan Heck
K
Krystel K. Castillo-Villar *
A
Adel Alaeddini
DOI:10.1016/j.cie.2026.112306delete
delete原文链接
delete原文求助
delete分享
delete收藏
摘要

摘要

En 中文
基于深度自编码器-高斯过程模型的跨打印机可复制性研究。 预测不同打印机间3D打印件的尺寸偏差。 在预测实际零件尺寸偏差方面优于基准机器学习模型。 实现增材制造过程的数据驱动优化。 为跨多个打印机单元减少废品和返工提供预测性见解。
Keyword:
Additive manufacturing
Machine-to-machine reproducibility
Autoencoder
Gaussian Process
Multi-domain learning

期刊

C
Computers & Industrial Engineering
IF:
6.5
论文数:
597
被引数:
0

机构

U
University of Texas at San Antonio
学者数:
654
论文数: 411
被引数: 0
S
Southern Methodist University
学者数:
3.0K
论文数: 3.5K
被引数: 3.9K
引用论文

引用论文

Printing the Future—Updates in 3D Printing for Surgical Applications
err2018-07-30
err0
errOAAI
errDekel Shilo; Omri Emodi; Ori Blanc; Dani Noy; Adi Rachmiel
err分享
err收藏
The deep kernelized autoencoder深度核化自动编码器
err2018-10-01
err13
errOAAI
errKampffmeyer, Michael; Lokse, Sigurd; Bianchi, Filippo M.; Jenssen, Robert; Livi, Lorenzo
err分享
err收藏
Toward Polymeric and Polymer Composites Impeller Fabrication
err2021-12-28
err0
errOAAI
errNader Zirak; Mohammadali Shirinbayan; Michael Deligant; Abbas Tcharkhtchi
err分享
err收藏
Machine learning in polymer additive manufacturing: a review聚合物增材制造中的机器学习:综述
err2024-09-06
err0
errOAAI
errMohammad Hossein Nikooharf; Mohammadali Shirinbayan; Mahsa Arabkoohi; Nadia Bahlouli; Joseph Fitoussi; Khaled Benfriha
err分享
err收藏
Predicting the Optimal Input Parameters for the Desired Print Quality Using Machine Learning
err2022-12-16
err9
errOAAI
errRatnavel, Rajalakshmi; Viswanath, Shreya; Subramanian, Jeyanthi; Selvaraj, Vinoth Kumar; Prahasam, Valarmathi; Siddharth, Sanjay
err分享
err收藏
学者 查看更多内容