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Quick roughness evaluation of cut edges using a convolutional neural network

 
: Stahl, Janek; Jauch, Christian

:

Cudel, Christophe ; Society of Photo-Optical Instrumentation Engineers -SPIE-, Bellingham/Wash.:
14th International Conference on Quality Control by Artificial Vision 2019 : 15-17 May 2019, Mulhouse, France
Bellingham, WA: SPIE, 2019 (Proceedings of SPIE 11172)
ISBN: 978-1-5106-3053-6
ISBN: 978-1-5106-3054-3
Paper 111720P, 8 S.
International Conference on Quality Control by Artificial Vision <14, 2019, Mulhouse>
Englisch
Konferenzbeitrag
Fraunhofer IPA ()
Bildverarbeitung; convolutional neural network; maschinelles Lernen; Oberflächenrauheit; Schnittkante

Abstract
In sheet metal production the quality of a cut is determined by the quality of the cut edge and is of crucial importance. One parameter affecting the quality of the cut edge surface is its roughness. In order to determine the roughness, the depth information is required. The common methods for acquiring depth information are very time consuming and therefore not suitable for a quick roughness evaluation. We present a method for a quick roughness evaluation by means of 2D image processing. It is shown that, given a proper dataset, a convolutional neural network can be trained to identify image features that correlate highly with the roughness of the edge surface and learn how to weight these features correctly. This makes the neural network capable of providing a quick and accurate statement about the roughness of the edge surface based on an image.

: http://publica.fraunhofer.de/dokumente/N-552509.html