Novel machine learning-driven multi-objective optimization method for EDM trajectory planning of distorted closed surfaces

Highly distorted closed surfaces pose significant challenges for machining trajectory planning due to their intricate surface constraints and closed structures. Despite these challenges, components with such features are prevalent in industries like aerospace. This paper presents a machine learning-driven multi-objective optimization method for electrical discharge machining (EDM) trajectory planning of highly distorted closed surfaces. The method transforms the structural design of forming electrodes and trajectory planning into a multi-objective decision problem. And a discrete point trajectory planning method, guided by surface average curvature, is employed to determine the optimal position and orientation of the electrode. Additionally, an elite dataset, generated using the Monte Carlo method and Arena's Principle, is utilized to train an artificial neural network (ANN). This network predicts hyperparameters for the nonlinear optimization problem. Based on the proposed method, a multi-objective optimization model is formulated for an integral shrouded blisk, considering minimization of iteration count, axial motion, and maximization of machining surface quality. The Pareto front is utilized to obtain the optimal EDM trajectory. Experimental results demonstrate a 17.38 % reduction in the overall machining cycle duration using this trajectory, and the surface roughness and profile accuracy satisfy the design specifications, which proves the effectiveness of this method.