Ultralight Artillery Cradle Structure Optimization

Based on the study of lightweight artillery home and abroad, this thesis is mainly focused on improvement and optimization of the artillery cradle to meet the requirements of weight reduction of large-caliber ultra lightweight artillery. After the structure optimal design, not only the stiffness of the cradle is obviously improved, but also the weight is lighter than the original one.First of all, the stiffness and strength of the original cradle is analyzed by the finite element analysis method. According to the problem of stiffness lack and overweight of the original cradle, the original structural form is improved with experiential design.Secondly, based on the improved structure, a multi-objective optimization model of cradle was set up based on the finite element method and optimization method for the design requirement of mass reduction and stiffness improving during the ultra lightweight artillery cradle design process. The mass and stiffness of the cradle are defined as two optimization objectives. The strength of the cradle is defined as constraint. The structural sizes and plank thickness of cradle are defined as design variables, and the improved non-dominated sorting genetic algorithm (NSGA-Ⅱ) is employed as the optimization method. By optimal solving, a Pareto solution set of cradle is achieved, and the cradle designed according to this solution set has a better structure performance.At last, topology optimization is carried out after size and shape optimization, in order to reduce the mass of the cradle to meet the requirements of weight reduction. The optimization objective is the mass of the cradle, and the stiffness of the cradle is defined as constraint. Analyzed by the variable density method, density cloud picture of the design area is attained and topology structural model is achieved after low density area is wiped off. By the finite element analysis, the stiffness of the cradle optimized with the topology optimization method is almost the same as the one before optimizing, and the weight has some reduction. The result improves that the topology optimization is reasonable.