An Approach for the Simulation of Ground and Honed Technical Surfaces for Training Classifiers

Abstract

Training of neural networks requires large amounts of data. Simulated data sets can be helpful if the data required for the training is not available. However, the applicability of simulated data sets for training neuronal networks depends on the quality of the simulation model used. A simple and fast approach for the simulation of ground and honed surfaces with predefined properties is being presented. The approach is used to generate a diverse data set. This set is then applied to train a neural convolution network for surface type recognition. The resulting classifier is validated on the basis of a series of real measurement data and a classification rate of >85% is achieved. A possible field of application of the presented procedure is the support of measurement technicians in the standard-compliant evaluation of measurement data by suggestion of specific data processing steps, depending on the recognized type of manufacturing process.