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The influence of experimental design method on design space exploration

机译:实验设计方法对设计空间探索的影响

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摘要

Simulation-based product design optimization includes processes of sampling using design of experiment (DOE), numerical simulation and construction of optimization model. The suitable DOE method is the one that can generate a set of sample points that better expresses the characteristics of the design space, making the design optimization process more accurate and efficient. We perform design space exploration and focus on the influence of DOE method on data mining. Firstly, different DOE methods are used to generate sample points. Secondly, design space exploration is carried out using rough set theory. Then analyze the effect of space exploration from the aspect of accuracy, the degree of space reduction, the number of sample points required and the like, after which an appropriate experimental design method for the problem is determined. The results show that in the problem of data mining for design optimization, uniform design is superior to Latin hypercube design, which lays the foundation for further application of uniform design in product design optimization.
机译:基于仿真的产品设计优化包括使用实验设计(DOE)进行抽样的过程,数值仿真和优化模型的构建。合适的DOE方法是可以生成一组采样点的方法,该采样点可以更好地表达设计空间的特征,从而使设计优化过程更加准确和高效。我们进行设计空间探索,并专注于DOE方法对数据挖掘的影响。首先,使用不同的DOE方法生成采样点。其次,利用粗糙集理论进行设计空间探索。然后从准确性,空间缩减程度,所需采样点数量等方面分析空间探索的效果,然后确定针对该问题的适当实验设计方法。结果表明,在设计优化的数据挖掘问题中,统一设计优于拉丁超立方体设计,这为统一设计在产品设计优化中的进一步应用奠定了基础。

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