Hierarchical data models improve the accuracy of feature level predictions for additively manufactured parts-Reference-Cited by-同舟云学术

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Hierarchical data models improve the accuracy of feature level predictions for additively manufactured parts

Published:2022-03 Issue: Volume:51 Page:102621
ISSN:2214-8604
Container-title:Additive Manufacturing
language:en
Short-container-title:Additive Manufacturing

Author:

Yang Yuhang,McGregor Davis J.,Tawfick Sameh,King William P.,Shao Chenhui

Funder

Zhejiang University

University of Illinois at Urbana-Champaign

Publisher

Elsevier BV

Subject

Industrial and Manufacturing Engineering,Engineering (miscellaneous),General Materials Science,Biomedical Engineering

Reference55 articles.

1. Additive manufacturing-enabled part count reduction: a lifecycle perspective;Yang;J. Mech. Des.,2018

2. Fused deposition modeling for unmanned aerial vehicles (uavs): a review;Klippstein;Adv. Eng. Mater.,2018

3. Heat transfer enhancement of internal laminar flows using additively manufactured static mixers;Kwon;Int. J. Heat. Mass Transf.,2019

4. 3d plate-lattices: an emerging class of low-density metamaterial exhibiting optimal isotropic stiffness;Tancogne-Dejean;Adv. Mater.,2018

5. Additive manufacturing (3d printing): a review of materials, methods, applications and challenges;Ngo;Compos. Part B: Eng.,2018

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1. Federated learning enables privacy-preserving and data-efficient dimension prediction and part qualification across additive manufacturing factories;Journal of Manufacturing Systems;2024-06

2. Detecting and classifying hidden defects in additively manufactured parts using deep learning and X-ray computed tomography;Journal of Intelligent Manufacturing;2024-05-24

3. Hybrid physics-guided data-driven modeling for generalizable geometric accuracy prediction and improvement in two-photon lithography;Journal of Manufacturing Processes;2024-01

4. Cross-section geometry prediction for laser metal deposition layer-based on multi-mode convolutional neural network and multi-sensor data fusion;Journal of Manufacturing Processes;2023-12

5. Automated deviation-aware landmark selection for freeform product accuracy qualification in 3D printing;IISE Transactions;2023-11-08

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