Anomaly Detection in Fused Filament Fabrication Using Machine Learning-Reference-Cited by-同舟云学术

Anomaly Detection in Fused Filament Fabrication Using Machine Learning

Published:2022-01-05 Issue: Volume: Page:
ISSN:2329-7662
Container-title:3D Printing and Additive Manufacturing
language:en
Short-container-title:3D Printing and Additive Manufacturing

Author:

Goh Guo Dong¹^ORCID,Hamzah Nur Muizzu Bin¹,Yeong Wai Yee¹²³

Affiliation:

1. Singapore Centre for 3D Printing, School of Mechanical & Aerospace Engineering, Nanyang Technological University Singapore, Singapore, Singapore.

2. HP-NTU Digital Manufacturing Corporate Lab, Nanyang Technological University, Singapore, Singapore.

3. NTU Institute for Health Technologies, Nanyang Technological University, Singapore, Singapore.

Publisher

Mary Ann Liebert Inc

Subject

Industrial and Manufacturing Engineering,Materials Science (miscellaneous)

Link

https://www.liebertpub.com/doi/pdf/10.1089/3dp.2021.0231

Reference32 articles.

1. Process–Structure–Properties in Polymer Additive Manufacturing via Material Extrusion: A Review

2. 2. Goh GD, Yeong WY, Mode I interlaminar fracture toughness of additively manufactured carbon fibre thermoplastic. In: Proceedings of the 3rd International Conference on Progress in Additive Manufacturing. Chua CK (Ed), Nanyang Technological University, Singapore. 2018; pp. 505–510.

3. Fused Filament Fabrication of Biodegradable PLA/316L Composite Scaffolds: Effects of Metal Particle Content

4. 3D Printing of Carbon Fiber Composite: The Future of Composite Industry?

5. Comparison of Stainless Steel 316L Parts Made by FDM- and SLM-Based Additive Manufacturing Processes

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