Abstract
This paper presents a hybridization model of support vector machine (SVM) and grey relational analysis (GRA) in predicting surface roughness value of abrasive water jet (AWJ) machining process. The influential factors of five process parameters in AWJ, namely traverse speed, water jet pressure, standoff distance, abrasive grit size and abrasive flow rate, need to be analyzed using GRA approach. Then, the irrelevance factors of process parameters are eliminated. There is a need of determining the influential factors of process parameters to the surface roughness as to develop a robust prediction model. GRA acts as feature selection method in preprocessing process of hybrid grey relational-support vector machine (GR-SVM) prediction model. Efficiency of the proposed model is demonstrated. GR-SVM presents more accurate result than conventional SVM as it removes the redundant features and irrelevant element from the experimental datasets.
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Acknowledgements
Special appreciation to reviewer(s) for useful advices and comments. The authors greatly acknowledge the Research Management Centre, UTM and Ministry of Higher Education Malaysia (MOHE) for financial support through the Exploratory Research Grant Scheme (ERGS) No. J13000078284L003.
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A comment to this article is available at http://dx.doi.org/10.1007/s11012-016-0542-8.
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Mat Deris, A., Mohd Zain, A. & Sallehuddin, R. Hybrid GR-SVM for prediction of surface roughness in abrasive water jet machining. Meccanica 48, 1937–1945 (2013). https://doi.org/10.1007/s11012-013-9710-2
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DOI: https://doi.org/10.1007/s11012-013-9710-2