Overview of Support Vector Machine in Modeling Machining Performances

Abstract

In machining, the process of modeling and optimization are challenging tasks and need proper approaches to qualify the requirements in order to produce high quality of products with less cost estimation. There are a lot of modeling techniques that have been discovered by researches. In the recent years the trends were towards modeling of machining using computational approaches such as support vector machine (SVM), artificial neural network (ANN), genetic algorithm (GA), artificial bee colony (ACO) and particle swarm optimization (PSO). This paper reviews the application of SVM, classified as one of the popular trends in modeling techniques for both types of machining operations, conventional and modern machining. Generally, support vector machine is a powerful mathematical tool for data classification, regression and function estimation and also widely used for modeling machining operations. In SVM, there are several types of kernel function that used in SVM training parameters such as linear, polynomial, radial basis function (RBF), sigmoid and Gaussian kernel function. Review shows that RBF kernel function was widely applied in SVM as a kernel function in modeling machining performances.