Surface Roughness Prediction in CNC Hole Turning of 3X13 Steel using Support Vector Machine Algorithm

Abstract

This paper presents research on a prediction method of surface roughness in the hole turning process of 3X13 steel. The experimental matrix was designed by using the Central Composite Design (CCD) with four input parameters including cutting speed, feed rate, cutting depth, and tool nose radius. Using the response surface method (RSM), a quadratic polynomial model was proposed to predict the surface roughness. Besides, another method that was used to predict surface roughness was the Support Vector Machine (SVM) algorithm. Using SVM, the predicted surface roughness was more accurate than that one when predicting surface roughness using RSM method. Using RSM, the mean absolute error and mean square error between experimental and expect results were 13.37 % and 3.93 %, respectively. While, using SVM, these values were only 2.80 % and 0.17 %, respectively. The SVM can be used to improve the prediction accuracy of surface roughness in hole turning process of 3X13 steel.

Integrating Taguchi Method and Support Vector Machine for Enhanced Surface Roughness Modeling and Optimization

“…The prediction results show the minimum Ra value can be achieved using SVM approach. Do Duc et al [22] predicted the Ra value of 3X13 steel material in CNC hole turning process. The machining experimental was conducted based on DOE with feed rate, cutting speed, tool nose radius and cutting depth as the input parameters.…”

Multiple Response Prediction and Optimization in Thin-Walled Milling of 6061 Aluminum Alloy

“…However, these methods have a common disadvantage, which is the optimal value set is one of the experimental values. This means that these techniques select one of the data sets that have been used, and in many cases, they are not the best [18][19][20].…”