摘要
板料成形优化中存在一些缺陷问题,如一步法与优化算法相结合时精度低、增量法与优化算法直接结合时效率低以及通过传统构造代理模型的方法构造高精度的代理模型时需要大量增量法模拟结果。为了充分发挥一步法和增量法各自的优点,提出采用变复杂度方法通过较少次数的试验样本点数据先在一步法和增量法间建立一个差值补偿响应面模型。通过一步法和差值补偿响应面模型构造新的试验样本点,在新构样本点和原有用增量法计算的样本点数据基础上建立移动最小二乘法代理模型,建立的移动最小二乘法代理模型精度和效率都较高。利用粒子群优化算法对移动最小二乘法代理模型进行优化求解。将该方法应用到了汽车某内板的成形性优化中,优化的结果显著地提高了板料的成形性。算例表明,该方法具有较高的精度和较强的工程实用性。
There are some defects in sheet metal forming optimization, such as one-step method combined with optimization algorithm results in low precision, incremental method directly combined with optimization algorithm results in low efficiency, and constructing high-precision agent model through traditional method requires a lot of simulation results of incremental method. In order to take the advantage of both one-step method and incremental method, variable complexity method is adopted to establish a response surface model for difference compensation between one-step method and incremental method through less-frequency test sample point data. New test sample points are constructed through one-step method and response surface model for difference compensation. Moving least-square agent model is constructed on the basis of both new and original sample point data. The established moving least-square agent model has high precision and efficiency. Particle swarm optimization is used for optimization solution of the said agent model. The method is applied to metal forming optimization of automobile inner panel, and the result of optimization improves the sheet metal formability. The numeral example indicates that this method has high precision and strong engineering practicability.
出处
《机械工程学报》
EI
CAS
CSCD
北大核心
2009年第9期201-209,共9页
Journal of Mechanical Engineering
基金
国家杰出青年基金(50625519)
长江学者和创新团队发展计划资助项目
关键词
变复杂度
板料成形
优化
移动最小二乘法
Variable complexity model Sheet metal forming Optimization Moving least square method
