Full Content is available to subscribers

Subscribe/Learn More  >

Broaching Tool Degradation Characterization Based on Functional Descriptors

[+] Author Affiliations
Wenmeng Tian, Jaime A. Camelio

Virginia Tech, Blacksburg, VA

Lee J. Wells

Western Michigan University, Kalamazoo, MI

Paper No. MSEC2016-8781, pp. V002T04A030; 10 pages
  • ASME 2016 11th International Manufacturing Science and Engineering Conference
  • Volume 2: Materials; Biomanufacturing; Properties, Applications and Systems; Sustainable Manufacturing
  • Blacksburg, Virginia, USA, June 27–July 1, 2016
  • Conference Sponsors: Manufacturing Engineering Division
  • ISBN: 978-0-7918-4990-3
  • Copyright © 2016 by ASME


With rapid advancements in sensing technologies and computation capabilities, high-resolution machine vision data have become available for various manufacturing processes. For machining, the use of machine vision data has shown great promise in machining tool condition monitoring, a critical factor for final product quality. Extensive research has been performed on wear characterization using intensity-based methods, but limited work has made use of process knowledge for image processing phases. Additionally, previous work focuses on single cutting edge machining tools, but no methods have been proposed for multiple cutting edge machining tools, such as broaches. In this paper, a process knowledge-based image filtering method is proposed to eliminate within-image and between-image noise to obtain effective wear region(s) for each cutting edge on a broach. In addition, these wear regions across multiple cutting edges are jointly described by fitting their relationship with each cutting edge’s respective chip load. Finally, the extracted model parameters are used for unsupervised learning to determine the entire tool’s degradation levels from a training dataset. A case study is introduced to show the effectiveness of the proposed methodology using a hexagonal broach.

Copyright © 2016 by ASME



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