基于神经网络响应面的复合材料结构优化设计

用正交试验设计的方法选择样本点构建神经网络响应面,将神经网络响应面作为优化的目标函数或约束条件,加上其它常规约束条件建立优化模型,应用遗传算法 (GA) 进行优化,形成一套适用于复杂结构设计的高效优化方法。以复合材料帽型加筋板的重量优化问题为例,建立了加筋板模型的重量响应面目标函数、强度和稳定性响应面约束条件;并用PATRAN/NASTRAN进行有限元计算,获取用于响应面训练的样本点数值。算例表明:该方法能以很少的有限元分析次数,取得高精度的响应面近似模型,并且使优化效率大大提高。 

COMPOSITE STRUCTURAL OPTIMIZATION DESIGN BASED ON NEURAL NETWORK RESPONSE SURFACES

To construct neural-network response surfaces for composite structural optimal design,the Orthotropic Experimental Method(OEM)was used to select the most appropriate structural analysis sample points.The constructed response surfaces were used as the objective function or constraint conditions.Together with other conventional constraints,they form an optimization design model which can be solved by using genetic algorithm(GA).This approach is highly applicable for complex composite structural design.Taking a hat-stiffened composite plate as example,the weight response surface was developed as the objective function,and strength and stability response surfaces as constraints;all these neural networks were trained by PATRAN/NASTRAN computation.The optimization results illustrate that it can significantly reduce the cycles of FEM analysis and achieve highly accurate response approximation results.And eventually,the approach can greatly raise the efficiency of the optimization process.