Force-based tool wear estimation for milling process using Gaussian mixture hidden Markov models

Force-based tool wear estimation for milling process using Gaussian mixture hidden Markov models Tool wear monitoring system is of vital importance for the guarantee of surface integrity and manufacturing effectiveness. To overcome the weaknesses of neural networks, a new tool wear estimation model based on Gaussian mixture hidden Markov models (GMHMM) is presented. Nine types of time-domain features are extracted from the milling force signals which are obtained under four sorts of tool wear state. Besides, the sensitive features which can indicate the tool wear states accurately are selected out by correlation analysis. To test the effectiveness of the presented model, the selected sensitive features serve to identify the tool wear states by utilizing GMHMM and back-propagation neural network (BPNN), respectively. Moreover, the identification performance of GMHMM under the combinations of various numbers of Gaussian mixtures and various lengths of observation sequence is analyzed to verify the practicability of the presented tool wear model. The experimental results show that the GMHMM-based model can identify the tool wear states effectively and GMHMM outperforms the BPNN model in accuracy and stability. This method lays the foundation on tool wear monitoring in real industrial settings. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png The International Journal of Advanced Manufacturing Technology Springer Journals

Force-based tool wear estimation for milling process using Gaussian mixture hidden Markov models

Loading next page...
 
/lp/springer_journal/force-based-tool-wear-estimation-for-milling-process-using-gaussian-T6Rde5uCql
Publisher
Springer London
Copyright
Copyright © 2017 by Springer-Verlag London
Subject
Engineering; Industrial and Production Engineering; Media Management; Mechanical Engineering; Computer-Aided Engineering (CAD, CAE) and Design
ISSN
0268-3768
eISSN
1433-3015
D.O.I.
10.1007/s00170-017-0367-1
Publisher site
See Article on Publisher Site

References

You’re reading a free preview. Subscribe to read the entire article.


DeepDyve is your
personal research library

It’s your single place to instantly
discover and read the research
that matters to you.

Enjoy affordable access to
over 12 million articles from more than
10,000 peer-reviewed journals.

All for just $49/month

Explore the DeepDyve Library

Unlimited reading

Read as many articles as you need. Full articles with original layout, charts and figures. Read online, from anywhere.

Stay up to date

Keep up with your field with Personalized Recommendations and Follow Journals to get automatic updates.

Organize your research

It’s easy to organize your research with our built-in tools.

Your journals are on DeepDyve

Read from thousands of the leading scholarly journals from SpringerNature, Elsevier, Wiley-Blackwell, Oxford University Press and more.

All the latest content is available, no embargo periods.

See the journals in your area

Monthly Plan

  • Read unlimited articles
  • Personalized recommendations
  • No expiration
  • Print 20 pages per month
  • 20% off on PDF purchases
  • Organize your research
  • Get updates on your journals and topic searches

$49/month

Start Free Trial

14-day Free Trial

Best Deal — 39% off

Annual Plan

  • All the features of the Professional Plan, but for 39% off!
  • Billed annually
  • No expiration
  • For the normal price of 10 articles elsewhere, you get one full year of unlimited access to articles.

$588

$360/year

billed annually
Start Free Trial

14-day Free Trial