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中文摘要：
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。
英文摘要：
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.
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Cell-Based Smoothed Radial Point Interpolation Method for Contact Problems in Metal Forming Analysis
Optimisation of sheet metal forming processes by the use of adaptive intelligent sampling scheme bas
A linearly conforming radial point interpolation method (LC-RPIM) for contact problems in metal form
The least square support vector regression coupled with parallel sampling scheme metamodeling techni
Optimization of drawbead design in sheet metal forming based on intelligent sampling by using respon
A Metamodel Optimization Methodology Based on Multilevel Fuzzy Clustering Space Reduction Strategy a
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