Reliable multi-objective optimization of high-speed WEDM process based on Gaussian process regression

Abstract

The paper discusses the development of reliable multi-objective optimization based on Gaussian process regression (GPR) to optimize the high-speed wire-cut electrical discharge machining (WEDM-HS) process, considering mean current, on-time and off-time as input features and material remove rate (MRR) and Surface Roughness (SR) as output responses. In order to achieve an accurate estimation for the nonlinear electrical discharging and thermal erosion process, the multiple GPR models due to its simplicity and flexibility identify WEDM-HS process with measurement noise. Objective functions of predictive reliability multi-objectives optimization are built by probabilistic variance of predictive response used as empirical reliability measurement and responses of GPR models. Finally, the cluster class centers of Pareto front are the optional solutions to be chosen. Experiments on WEDM-HS (DK7732C2) are conducted to evaluate the proposed intelligent approach in terms of optimization process accuracy and reliability. The experimental result shows that GPR models have the advantage over other regressive models in terms of model accuracy and feature scaling and probabilistic variance. Given the regulable coefficient parameters, the experimental optimization and optional solutions show the effectiveness of controlling optimization process to acquire more reliable optimum predictive solutions.