期刊
MANUFACTURING LETTERS
卷 22, 期 -, 页码 11-15出版社
ELSEVIER
DOI: 10.1016/j.mfglet.2019.09.005
关键词
Additive manufacturing; Fused modeling deposition; Computer vision; Deep learning; Convolutional neural networks
资金
- Extreme Science and Engineering Discovery Environment (XSEDE) by National Science Foundation [ACI-1548562]
- NVIDIA GPU Seed Grant
- Johnson & Johnson WiSTEM2D Scholars Award
- Amazon Research Award
Fused deposition modeling, a widely used additive manufacturing process, currently faces challenges in printed part quality such as under-extrusion and over-extrusion. In this paper, a real-time monitoring and autonomous correction system is developed, where a deep learning model and a feedback loop is used to modify 3D-printing parameters iteratively and adaptively. Results show that our system is capable of detecting in-plane printing conditions and in-situ correct defects faster than the speed of a human's response. The fundamental elements in the framework proposed can be extended to various 3D-printing technologies to reliably fabricate high-performance materials in challenging environments without human interaction. (C) 2019 Society of Manufacturing Engineers (SME). Published by Elsevier Ltd. All rights reserved.
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