Online Convolutional Neural Network-based anomaly detection and quality control for Fused Filament Fabrication process-Reference-Cited by-同舟云学术

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Online Convolutional Neural Network-based anomaly detection and quality control for Fused Filament Fabrication process

Published:2021-03-04 Issue:2 Volume:16 Page:160-177
ISSN:1745-2759
Container-title:Virtual and Physical Prototyping
language:en
Short-container-title:Virtual and Physical Prototyping

Author:

Lyu Jiaqi¹^ORCID,Manoochehri Souran¹^ORCID

Affiliation:

1. Department of Mechanical Engineering, Stevens Institute of Technology, Hoboken, USA

Publisher

Informa UK Limited

Subject

Industrial and Manufacturing Engineering,Computer Graphics and Computer-Aided Design,Modeling and Simulation,Signal Processing

Link

https://www.tandfonline.com/doi/pdf/10.1080/17452759.2021.1905858

Reference52 articles.

1. Surface Roughness Quality and Dimensional Accuracy—A Comprehensive Analysis of 100% Infill Printed Parts Fabricated by a Personal/Desktop Cost-Effective FDM 3D Printer

2. Application of Machine Learning in 3D Bioprinting: Focus on Development of Big Data and Digital Twin

3. Anderson, E. H. 2019. The effect of porosity on mechanical properties of fused deposition manufactured polymers and composites.

4. Chest pathology detection using deep learning with non-medical training

5. Method for registration of 3-D shapes

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