Structure optimization of DTH hammer piston based on RBF neural network and WSO algorithm

Abstract

To address the issue of piston fracture due to insufficient strength in the Down-The-Hole (DTH) hammer. The effect of the pressure state of the gas chamber on the piston motion is considered. A piston motion model is established based on the steady flow energy equation and the working principle of the piston. The finite element method is used to analyze the dynamic response of the piston during the impact of the drill bit. RBF neural network is employed to construct a surrogate model for the relationship between piston structural parameters (front-end diameter, front-end length, and center bore) and piston strength and performance (impact energy, frequency, and gas consumption). By combining this model with the white shark optimization search algorithm, the design of the DTH hammer’s piston structure parameters is optimized. The minimum piston mass is the objective function. Impact energy, frequency, air consumption and stress are constraint factors. The effect of piston life is considered while meeting rock drilling efficiency. The optimization results show that the stresses in the optimized piston structure are reduced by 13%. The proposed RBF performance prediction model combined with the optimal search algorithm can significantly improve the optimization efficiency.