Support vector machine-based unified learning system for prediction of multiple responses in AWJM of borosilicate glass and SEM study. (2016) - Aich, Ushasta; Banerjee, Simul; Bandyopadhyay, Asish; Das, Probal Kumar
Modelling of responses in any manufacturing process is helpful for working in virtual world. As such, effective model development of stochastic processes working on heterogeneous materials is reasonably difficult. Hence, a robust unified learning system, multi-objective modelling with SVM, is proposed in this work to study the gross erosion behaviour of borosilicate glass in abrasive water jet machining. In this study, experiments are conducted on borosilicate glass with variation of the control parameters - water pressure, abrasive flow rate, traverse speed and standoff distance. Two process responses - material removal rate (MRR) and depth of cut (DOC) are trained through support vector machine (SVM)-based learning system for regression. An optimised single set of internal parameters of SVM, that would predict both MRR and DOC with their respective Lagrange multipliers, is estimated by minimising the training errors with the help of particle swarm optimisation (PSO) procedure. A modification of PSO is also proposed in this article. Further, scanning electron micrographs of cut wall are qualitatively examined to reveal the possible erosion behaviour of the amorphous material - borosilicate glass.