Lightweight Design Of Ball Mill Cylinder Structure Based On Genetic Polymerization Agent Model

Abstract

To address the issues of strong empirical dependence and low computational efficiency in traditional ball mill cylinder design, this study proposes a lightweight design methodology integrating multiple response surface models with finite element parametric simulation technology. Based on the equivalent density method, stress-strain characteristics are obtained through simplified cylinder structural modeling and finite element static analysis. Parametric finite element simulations are employed to generate sample data, The optimal response surface model is determined by comparing the goodness-of-fit (using evaluation metrics such as the coefficient of determination R², root mean square error RMSE, etc.) among neural network, Kriging, and genetic aggregation methods. Key structural parameters of the cylinder are identified through sensitivity analysis using data generated from the optimal response surface, enabling the construction of a lightweight mathematical model that is solved using a multi-objective genetic algorithm. Experimental validation on an MQY5585 ball mill demonstrates that the proposed method achieves an 8.04% reduction in cylinder mass and a 12.28% decrease in maximum deformation while maintaining equivalent stress within permissible safety limits.