Gaussian Process Regression for the Prediction of Cable Bundle Characteristics

In modern cars, a huge number of different cables can be found. They are typically combined in hoses and bundles in various different ways. For virtual product development and simulation-based design, it is necessary to know the characteristic physical parameters, like effective bending or torsion stiffness, of these cable systems. In early stages of the development process as well as for highly customized individual cable configurations, measuring effective stiffness properties is, however, often very challenging. In this contribution, we show results from our current research activities aiming at data-based modeling and estimating effective stiffness parameters for cable bundles. On the basis of an available dataset consisting of measured stiffness values for varying cable bundles, the overall goal is to identify a model out of this data that predicts bundle stiffness values for unknown bundles. We outline our approach to solve this nonlinear identification task with Gaussian Process Regression. Besides a short introduction to the industrial application area, we demonstrate and illustrate the applicability and prediction quality of Gaussian Process Regression for this task.