Please use this identifier to cite or link to this item: https://scholar.utcc.ac.th/handle/6626976254/3452
Title: Insitu work piece surface roughness estimation in turning
Authors: Kamarthi, S. 
Sultornsanee, S. 
Zeid, A. 
Issue Date: 2014
Publisher: Scopus
University of the Thai Chamber of Commerce
Source: S. Kamarthi, S. Sultornsanee, A. Zeid (2014) Insitu work piece surface roughness estimation in turning. IEEE International Conference on Automation Science and Engineering, 328-332.
Conference: IEEE International Conference on Automation Science and Engineering 
Abstract: This paper describes a method for inprocessestimation of surface roughness of the workpiece in a turning process from acoustic emission signals generated by the sliding friction between a graphite probe and the workpiece. Acoustic emission signals are transformed into recurrence plots and a set of recurrence statistics are computed using the recurrence quantification analysis. The surface roughness parameters are estimated using an artificial neural network, taking the recurrence statistics of the acoustic emission signals as inputs. This method is verified by conducting an extensive set of experiments on AISI 1054 steel workpiece and K420 grade uncoated carbon inserts. We consider three surface roughness parameters for estimation, namely arithmetic mean, maximum peaktovalley roughness, and mean roughness depth. The estimation accuracy of the proposed method is in the range of 90.13% to 91.26%.
URI: https://scholar.utcc.ac.th/handle/6626976254/3452
Rights: This work is protected by copyright. Reproduction or distribution of the work in any format is prohibited without written permission of the copyright owner.
Appears in Collections:RSO: Conference Papers

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