Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/89656
Title: In-process surface roughness estimation model for compliant abrasive belt machining process
Authors: Pandiyan, Vigneashwara
Tjahjowidodo, Tegoeh
Samy, Meena Periya
Keywords: In-process Measurement
Roughness
DRNTU::Engineering::Mechanical engineering
Issue Date: 2016
Source: Pandiyan, V., Tjahjowidodo, T., & Samy, M. P. (2016). In-process surface roughness estimation model for compliant abrasive belt machining process. Procedia CIRP, 46, 254-257. doi:10.1016/j.procir.2016.03.126
Series/Report no.: Procedia CIRP
Abstract: Surface roughness inspection in robotic abrasive belt machining process is an off-line operation which is time-consuming. An in-process multi-sensor integration technique comprising of force, accelerometer and acoustic emission sensor was developed to predict state of the surface roughness during machining. Time and frequency-domain features extracted from sensor signals were correlated with the corresponding surface roughness to train the Support vector machines (SVM's) in Matlab toolbox and a classification model was developed. Prediction accuracy of the classification model shows proposed in-process surface roughness recognition system can be integrated with abrasive belt machining process for capping lead-time and is reliable.
URI: https://hdl.handle.net/10356/89656
http://hdl.handle.net/10220/47107
ISSN: 2212-8271
DOI: 10.1016/j.procir.2016.03.126
Rights: © 2016 The Authors. Published by Elsevier B.V. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).
Fulltext Permission: open
Fulltext Availability: With Fulltext
Appears in Collections:MAE Journal Articles

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