0

Full Content is available to subscribers

Subscribe/Learn More  >

A New Approach for Online Monitoring of Additive Manufacturing Based on Acoustic Emission

[+] Author Affiliations
Haixi Wu, Zhonghua Yu

Zhejiang University, Hangzhou, China

Yan Wang

Georgia Institute of Technology, Atlanta, GA

Paper No. MSEC2016-8551, pp. V003T08A013; 8 pages
doi:10.1115/MSEC2016-8551
From:
  • ASME 2016 11th International Manufacturing Science and Engineering Conference
  • Volume 3: Joint MSEC-NAMRC Symposia
  • Blacksburg, Virginia, USA, June 27–July 1, 2016
  • Conference Sponsors: Manufacturing Engineering Division
  • ISBN: 978-0-7918-4991-0
  • Copyright © 2016 by ASME

abstract

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.

Copyright © 2016 by ASME

Figures

Tables

Interactive Graphics

Video

Country-Specific Mortality and Growth Failure in Infancy and Yound Children and Association With Material Stature

Use interactive graphics and maps to view and sort country-specific infant and early dhildhood mortality and growth failure data and their association with maternal

NOTE:
Citing articles are presented as examples only. In non-demo SCM6 implementation, integration with CrossRef’s "Cited By" API will populate this tab (http://www.crossref.org/citedby.html).

Some tools below are only available to our subscribers or users with an online account.

Related Content

Customize your page view by dragging and repositioning the boxes below.

Related eBook Content
Topic Collections

Sorry! You do not have access to this content. For assistance or to subscribe, please contact us:

  • TELEPHONE: 1-800-843-2763 (Toll-free in the USA)
  • EMAIL: asmedigitalcollection@asme.org
Sign In