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Classifier Fusion for Acoustic Emission Based Tool Wear Monitoring

[+] Author Affiliations
Juil Yum, Tae Hyung Kim, Elijah Kannatey-Asibu, Jr.

University of Michigan, Ann Arbor, MI

Paper No. MSEC2012-7380, pp. 805-812; 8 pages
  • ASME 2012 International Manufacturing Science and Engineering Conference collocated with the 40th North American Manufacturing Research Conference and in participation with the International Conference on Tribology Materials and Processing
  • ASME 2012 International Manufacturing Science and Engineering Conference
  • Notre Dame, Indiana, USA, June 4–8, 2012
  • Conference Sponsors: Manufacturing Engineering Division
  • ISBN: 978-0-7918-5499-0
  • Copyright © 2012 by ASME


It is often difficult for a single classifier to achieve perfect classification during process monitoring. Sensor fusion enables the final decision to be improved, but uses voting methods, which usually do not perform well when there is a tie vote. In this paper, classifier fusion with class-weighted voting is investigated to further enhance the performance of monitoring systems. The overall performances of individual classifiers are used as the weighting factors to classifier fusion based on majority voting. When applied to tool wear monitoring of the coroning process, the classifier that was based on overall performance weighting improved the classification rate to 95.6% and the one based on state performance weighting showed 98.5% classification, compared to 87.7% for classifier fusion with unity weighting. A classifier fusion further increased performance from 98.5% to 99.7% by applying a penalty vote on the weighting factor.

Copyright © 2012 by ASME



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