T形管液压成形自适应径向基函数优化设计

The Loading Path Optimization for T-Shape Tube Hydroforming Using Adaptive Radial Basis Function

讨论T形管液压成形加载路径代理模型优化的设计.采用径向基函数(RBF)模型,引入自适应优化算法,通过逐步向静态RBF模型样本库中增加样本点的方法,提升最优点附近局部近似计算精度,进而提升全局精度并获得最优解.该方法兼顾了优化效率与计算精度.首先通过数值算例证明此方法应用于全局优化的有效性;然后,以管件与中间冲头接触面积最大为优化目标,以最大减薄率小于对标实验值、成形高度大于对标实验值为约束条件,通过拉丁方试验设计抽取一定数量的样本点并调用实际分析模型构建T形管液压成形加载路径优化自适应RBF模型,开展载荷路径优化设计.结果表明,在最小厚度与成形高度不变的情况下,T形管与中间冲头的接触面积较对标实验值提高了71.912%.

The study of surrogate model optimization of the loading path of a T-shape tube hydroforming is carried out in this paper. The adaptive optimization algorithm is introduced to radial basis function（RBF）. In order to improve approximate accuracy in the concerned local regions, the paper proposes to add sample points gradually into the sample database, and to obtain the globally optimal efficiency and accuracy. The effectiveness of the method in global optimization is demonstrated by a numerical example firstly, then, a- daptive radial basis function model for the T-shape tube hydroforming loading path optimization is con- structed, and optimization design is carried out. The contact areas of the tube and the counter punch are se lected to be the optimization target, the constraints are that the maximum thinning ratio is less than the experimental value, and the protrusion height is higher than the experimental value. The Latin hypercube design is used to obtain sample points, and the actual values of finite element analysis model of T-shape hydroforming are calculated. The loading path optimization design results are compared with the experi- mental values, and the result shows that the contact areas of T-shape tube and counter punch have im- proved by 71. 912-/00 under the condition that the minimum thickness and the protrusion height are main- tained.