Data mining and machine learning methods applied to a numerical clinching model

Numerical mechanical models used for design of structures and processes are very complex and high-dimensionally parametrised. The understanding of the model characteristics is of interest for engineering tasks and subsequently for an efficient design. Multiple analysis methods are known and available to gain insight into existing models. In this contribution, selected methods from various fields are applied to a real world mechanical engineering example of a currently developed clinching process. The selection of introduced methods comprises techniques of machine learning and data mining, in which the utilization is aiming at a decreased numerical effort. The methods of choice are basically discussed and references are given as well as challenges in the context of meta-modelling and sensitivities are shown. An incremental knowledge gain is provided by a step-by step application of the numerical methods, whereas resulting consequences for further applications are highlighted. Furthermore, a visualisation method aiming at an easy design guideline is proposed. These visual decision maps incorporate the uncertainty coming from the reduction of dimensionality and can be applied in early stage of design.