Modeling of delamination in drilling of glass fiber-reinforced polyester composite by support vector machine tuned by particle swarm optimization

Abstract

Surface damage in machining of fiber-reinforced polymer-based composites is almost unavoidable during manufacturing. Most often, machining operation—drilling causes delamination of composite surface that leads to the loss of quality of product. As a consequence, reasonably accurate prediction of delamination factor (Fd) of drilled hole emerges as a prerequisite during the product development stage and freezing the design before final production. However, stochastic nature of the response and heterogeneous material properties make the modeling difficult. In this article, one of the most advanced generalized learning-based technologies, support vector machine (SVM) which could read the underlying unseen effect of input factors on response, is applied for regression model developing of drilling response—Fd on glass fiber-reinforced polyester composite. Gaussian radial basis function and e-insensitive loss function are used as kernel functions and loss function, respectively. Particle swarm optimization (PSO) is modified, and modified PSO is employed to search the optimal combination of internal parameters of SVM for modeling of Fd. Model, thus developed, is validated with follow-up testing data sets. Based on estimated model, optimum input parameters for minimum Fd is further investigated using the procedure of modified particle swarm optimization.