Investigation of the Potential of Using Surrogate Models in the Gear Design Process

摘要

This paper was first presented at the International VDI Conference on Gears 2019, 3rd International Conference on High-Performance Plastic Gears 2019, 3rd International Conference on Gear Production 2019, Garching/Munich (VDI-Berichte 2355, 2019, VDI Verlag GmbH, Page 727-740). State of the Art. Surrogate models, also known as response surface models or metamodels, are approximation models, which are based on mathematical functions (Ref.1). In engineering, surrogate models are used to correlate the input and output variables of experiments and simulations (Refs. 2-10). This is especially true for very time-consuming, costly or high number of experiments/simulations. In this case, the surrogate model can be evaluated much faster in comparison to the experiment or complex simulation. This is most important for design space exploration or optimization where a high number of experiments of simulations is necessary (Ref.5). In order to reduce the time effort, the extensive simulation is only performed for a reduced number of parameter sets. These initial parameter sets are defined by means of methods of design of experiment (DOE), e.g., full-factorial sampling or latin hypercube sampling (Ref.11). For computational problems a latin hypercube sampling or the Monte-Carlo approach (random sampling) is often used to identify the initial parameter sets. Once the initial parameter sets are identified, the simulation is performed at these given points. The results of the simulation are used to fit a surrogate model to the given input variables in order to approximate the system behavior of the engineering system. Possible approximation types for surrogate models are shown in Figure 1. The most common modeling types are models based on radial basis functions (RBF), kriging models, also known as Gaussian process models, and models based on multivariate adaptive regression splines (MARS).