Despite its recent popularity, additive manufacturing (AM) still faces many technical challenges for the insufficiency of process reliability, controllability, and product quality. To enhance the process repeatability, effective in-situ monitoring methods for AM processes are needed. In this study, an online monitoring method for AM process failure detection is proposed, where acoustic emission (AE) is applied as the sensing technique. Its application to polymer material extrusion, also known as the technology of fused deposition modeling (FDM), is demonstrated. Experimental results show that the proposed monitoring method allows for the real time identification of major process failures. The occurring time of major failures and failure modes can be identified by analyzing the time- and frequency-domain features of AE hits respectively. A K-means clustering algorithm is applied to verify and demonstrate the classification procedure. The automated failure identification can reduce the waste of fabrication with enhanced machine intelligence.
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ASME 2016 11th International Manufacturing Science and Engineering Conference
June 27–July 1, 2016
Blacksburg, Virginia, USA
Conference Sponsors:
- Manufacturing Engineering Division
ISBN:
978-0-7918-4991-0
PROCEEDINGS PAPER
A New Approach for Online Monitoring of Additive Manufacturing Based on Acoustic Emission
Zhonghua Yu,
Zhonghua Yu
Zhejiang University, Hangzhou, China
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Yan Wang
Yan Wang
Georgia Institute of Technology, Atlanta, GA
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Haixi Wu
Zhejiang University, Hangzhou, China
Zhonghua Yu
Zhejiang University, Hangzhou, China
Yan Wang
Georgia Institute of Technology, Atlanta, GA
Paper No:
MSEC2016-8551, V003T08A013; 8 pages
Published Online:
September 27, 2016
Citation
Wu, H, Yu, Z, & Wang, Y. "A New Approach for Online Monitoring of Additive Manufacturing Based on Acoustic Emission." Proceedings of the ASME 2016 11th International Manufacturing Science and Engineering Conference. Volume 3: Joint MSEC-NAMRC Symposia. Blacksburg, Virginia, USA. June 27–July 1, 2016. V003T08A013. ASME. https://doi.org/10.1115/MSEC2016-8551
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