Full Content is available to subscribers

Subscribe/Learn More  >

An Anomaly Detection and Diagnosis Method Based on Real-Time Health Monitoring for Progressive Stamping Processes

[+] Author Affiliations
Huanyi Shui, Xiaoning Jin, Jun Ni

University of Michigan, Ann Arbor, MI

Paper No. MSEC2015-9420, pp. V002T05A021; 10 pages
  • ASME 2015 International Manufacturing Science and Engineering Conference
  • Volume 2: Materials; Biomanufacturing; Properties, Applications and Systems; Sustainable Manufacturing
  • Charlotte, North Carolina, USA, June 8–12, 2015
  • Conference Sponsors: Manufacturing Engineering Division
  • ISBN: 978-0-7918-5683-3
  • Copyright © 2015 by ASME


Progressive stamping processes have been applied to fabricate an extended range of products from centimeter-scale parts to meter-scale parts. The quality of stamped products may vary and be out of specification due to various anomalies during manufacturing process. Therefore, an effective online health monitoring and fault diagnosis technique is of great practical significance. This paper develops a two-stage systematic approach to enhance the fault detection and fault identification capability for the progressive stamping process with aggregated system-level tonnage signals. The first stage uses a combined Haar transform and power spectrum analysis to map features extracted from aggregated signals to individual operations. The second stage develops a two-step control chart strategy for anomaly detection and identification. The proposed method can improve the monitoring effectiveness and the quality assessment of individual operations based on an aggregated tonnage signal especially when single working range of different operations in the multi-station system are highly overlapped. The results show the method efficacy of quick and accurate anomaly detection and identification in real time.

Copyright © 2015 by ASME
Topics: Metal stamping



Interactive Graphics


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

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