Multiobjective optimization for the bed structure of a CNC gantry machine tool based on neural networks and intelligent optimization algorithms.

This study proposes a multiobjective optimization design method for the bed structure of CNC gantry machine tools to enhance their mechanical performance. A sensitivity analysis was first conducted to identify the key dimensions affecting the bed's mass and static and dynamic characteristics, which were then selected as optimization variables. Design of experiments was employed to obtain target values under various design variables, and the response surface method combined with neural network algorithms was utilized to approximate and validate the objective functions. Subsequently, the entropy weight method was applied to calculate the weight coefficients of multiple optimization objectives, establishing a comprehensive performance optimization model for the bed structure. Using MATLAB, three intelligent algorithms-simulated annealing, genetic algorithm, and particle swarm optimization-were employed to solve the optimization model. Comparative results before and after optimization demonstrated that the optimized bed structure achieved a maximum deformation reduction of 9.41%, a 5.75% increase in the first-order natural frequency, a 1.23% reduction in maximum stress, and a 0.64% decrease in mass. The proposed optimization method offers a novel approach for simultaneously reducing the weight of structural components while enhancing their static and dynamic performance.