NSGA-II Algorithm-Based Structural Parameters of Electric Pulse Rock-Breaking Electrode Bit Multi-Objective Optimization

Abstract

Abstract High-voltage electric pulse (HVEP) electrode bit has a considerable influence on the drilling and rock-breaking (RB) efficiency. HVEP electrode bit was systematically studied to optimize the structural parameters in order to improve RB efficiency. This paper analyzed the impact of main structural parameters on electric field strength (EFS) and depth of penetration (DOP) during high-voltage electric pulse drilling. A structural optimization method integrating back propagation (BP) neural network and genetic algorithm for HVEP electrode bit was proposed. The method mapped the complex nonlinear relationships among electrode distance, electrode cone angle, electrode grounding span, etc., and EFS and DOP by establishing a BP neural network model, and adopted the non-dominated sorting genetic algorithm-II (NSGA-II) to optimize the main structural parameters. The simulation data showed that the combined BP neural network/non-dominated sorting genetic algorithm-II (BP-NSGA-II) was an effective tool for optimizing the injection molding process. The multi-objective optimization of the structural parameters of the HVEP electrode bit based on the NSGA-II algorithm was crucial to direct the choice of the process parameters of the HVEP electrode bit, boost the RB efficiency, and lower the energy loss during drilling.