Full Content is available to subscribers

Subscribe/Learn More  >

Cutting Tool Condition Monitoring and Prediction Based on Dynamic Data Driven Approaches

[+] Author Affiliations
Zhenhua Wu

Virginia State University, Petersburg, VA

Paper No. MSEC2015-9346, pp. V001T02A110; 10 pages
  • ASME 2015 International Manufacturing Science and Engineering Conference
  • Volume 1: Processing
  • Charlotte, North Carolina, USA, June 8–12, 2015
  • Conference Sponsors: Manufacturing Engineering Division
  • ISBN: 978-0-7918-5682-6
  • Copyright © 2015 by ASME


In this paper, monitoring and prediction of cutting tool wear condition based on dynamic data driven approaches were investigated. Sensor signals obtained from the machining processes were processed through wavelet denoising to filter the noise un-related to cutting, features in time and frequency domains were extracted using classical signal processing approaches, and then were selected with Pearson correlation coefficient. The most related features were sent to the feature fusion approaches including neural network (NN), adaptive neural fuzzy inference system (ANFIS), or support vector regression (SVR) to estimate the tool wear. Statistics performance evaluation based on correlation coefficient (R2), average absolute error (AAE), and Se/Sy, as well as cross validation, selected the most proper feature fusion approach. Further, prediction models based on Bayesian model average were applied to predict the future tool wear. A case study based on the end mill experiment with signals of 3-axis cutting forces, 3-axis vibrations and acoustic emission, illustrated the proposed approach. It showed that ANFIS has the best estimation accuracy with the R2 of 0.99, AAE of 0.42, Se/Sy of 0.12, and cross validation error of 13.36. In the prediction stage, the prediction model has high prediction accuracy with all the experiment results covered by 95% confidence interval of prediction.

Copyright © 2015 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