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Technologies 2017, 5(4), 66; doi:10.3390/technologies5040066

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

Institute for Measurement and Sensor-Technology, University of Kaiserslautern, Gottlieb-Daimler-Straße, 67663 Kaiserslautern, Germany
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Received: 19 September 2017 / Revised: 5 October 2017 / Accepted: 11 October 2017 / Published: 14 October 2017
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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. View Full-Text
Keywords: technical surfaces; microstructured surfaces; grinding; honing; simulation of surfaces; classification of surfaces; convolutional neuronal networks technical surfaces; microstructured surfaces; grinding; honing; simulation of surfaces; classification of surfaces; convolutional neuronal networks
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).

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MDPI and ACS Style

Rief, S.; Ströer, F.; Kieß, S.; Eifler, M.; Seewig, J. An Approach for the Simulation of Ground and Honed Technical Surfaces for Training Classifiers. Technologies 2017, 5, 66.

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