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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.

Optimum control parameter settings in complex and stochastic type processes are one of the most challenging problems to the process engineers. As such, effective model development and determination of optimal operating conditions of abrasive water jet machining process (AWJM) are reasonably difficult. In this article new modifications are proposed on two single-objective optimisation techniques, simulated annealing and particle swarm optimisation and applied to optimise two response parameters in AWJM – material removal rate (MRR) and depth of cut (DOC) simultaneously. For the optimisation purpose statistical models are developed from experimental data obtained from AWJ machining of amorphous material – borosilicate glass. Optimum results are validated and it is suggested to employ particle swarm-based proposed multi-objective optimisation technique in present case for newly modelled system due to its fast convergence and low memory space requirement.