基于智能布点技术的近似模型优化研究及其在冲压成形中的应用  

Research on Intelligent Sampling Based Metamodel Optimization and Its Applications in Forming

作　　者：李恩颖[1] 李光耀[1] 

机构地区：[1]湖南大学汽车车身先进设计与制造国家重点试验室,长沙410082

出　　处：《机械工程学报》2009年第8期210-215,共6页Journal of Mechanical Engineering

基　　金：国家杰出青年基金(50625519);教育部新世纪优秀人才支持计划资助项目

摘　　要：针对基于统计学原理试验设计方法的缺陷,采用一种基于设计变量边界条件以及最优样本信息的智能布点方法,并结合移动最小二乘的响应面近似模型优化方法对非线性问题进行求优。为了验证该方法的有效性,采用该方法对非线性测试函数进行了极值求解。同目前主流的试验设计方法相比,其求解精度以及响应面拟合精度都有一定的提高。将该方法应用于薄板冲压成形体系中拉延筋的优化,得到预期的结果,并成功地用于实际产品成形。In view of the defects of design of experiment （DOE） methods based on probability theory, a boundary and best neighbor based intelligent sampling （BBNS） method is proposed. This sampling scheme also can be combined with moving least square （MLS） response surface （RS） metamodel method to optimize nonlinear problems. In order to verify the validity of this intelligent sampling method, this method is adopted to carry out extreme solving of nonlinear test function. Compared with current main DOE methods, this method shows certain improvement in solution precision and in response surface fitting precision. The intelligent sampling scheme coupled with MLS RSM is applied for optimization of draw bead in sheet forming system, and expected results are obtained. It is used successfully in the forming of actual products.

关 键 词：优化 智能布点 移动最小二乘 近似模型 响应面 

分 类 号：TG156[金属学及工艺—热处理]