Hybrid GR-SVM for prediction of surface roughness in abrasive water jet machining

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.     

A note of hybrid GR-SVM for prediction of surface roughness in abrasive water jet machining: a response

This paper is the responses for a note submitted by Dr. Antoni Wibowo based on the article entitle “Hybrid GR-SVM for prediction of surface roughness in abrasive water jet (AWJ) machining”. The author of the note pointed out some problems in the original paper. The paper presented a proposed hybridization approach of grey relational analysis and support vector machine in predicting surface roughness (Ra) in AWJ machining. We deny all the claims given by Dr. Wibowo based on the justifications stated in this paper.