A new data-driven design methodology for mechanical systems with high dimensional design variables. (March 2018) - Du, Xianping; Zhu, Feng
Highlights: A data mining design method was proposed to overcome the curse of dimensionality of structural design with high-dimensional design variables. The critical parameters identification and design domain reduction were realized simultaneously using this method. The newly developed method was implemented by designing a crashworthy car systematically, which is a multi-level complicated system. This method has superior performance over the conventional method in terms of accuracy and efficiency. Abstract: Complicated engineering products such as cars with a large number of components can be regarded as big data systems, where the vast amount of dependent and independent design variables must be considered systematically during the product development. To design such a system with high-dimensional design variables, this study aims at developing a novel methodology based on data mining theory, and it is implemented through designing a crashworthy passenger car, which is a multi-level (system – components) complicated system. Decision tree technique was used to mine the crash simulation datasets to identify the key design variables with most significant effect on the vehicular energy absorption response and determine the range of their values. In this way, the design space can be significantly reduced and the high-dimensional design problem is greatly simplified. The results suggest that the data mining based approach can be used to design a complicated structure with multiple parameters effectively and efficiently. Compared with the traditional design method, the new approach could simplify and speed up the design process without significant influence on the accuracy.