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Proceedings Paper

Development of a convolutional autoencoder using deep neuronal networks for defect detection and generating ideal references for cutting edges
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Paper Abstract

Cutting edges are of great importance in industry and especially in mechanical engineering. However, like other components, they wear out over time. The contour and in particular the cutting edge itself can be damaged over time or by other occurrences and be defective. If the traces of use or defects are small, they can be corrected by reworking. This means that the cutting edge can still be used by post-processing. To achieve this, it is necessary to measure the cutting edge. Subsequently, the error must be evaluated. This error should indicate whether and how far the cutting edge must be reworked. In order to carry out such an evaluation, ideal references of the cutting edge are necessary. If an ideal geometry of the cutting edge is available as a computer-aided design model, the evaluation is trivial. However, this only exists in very rare cases. Often the reference geometry must be formed on the basis of one measurement. This paper presents a possibility of reconstructing cutting edges and therefore a rating of this cutting edge. The reconstruction is based on neuronal networks, more precisely by convolutional neuronal networks.

Paper Details

Date Published: 21 June 2019
PDF: 7 pages
Proc. SPIE 11056, Optical Measurement Systems for Industrial Inspection XI, 1105623 (21 June 2019); doi: 10.1117/12.2525882
Show Author Affiliations
Abdullah Karatas, Technische Univ. Kaiserslautern (Germany)
Dorothea Kölsch, Technische Univ. Kaiserslautern (Germany)
Samuel Schmidt, Technische Univ. Kaiserslautern (Germany)
Matthias Eifler, Technische Univ. Kaiserslautern (Germany)
Jörg Seewig, Technische Univ. Kaiserslautern (Germany)

Published in SPIE Proceedings Vol. 11056:
Optical Measurement Systems for Industrial Inspection XI
Peter Lehmann; Wolfgang Osten; Armando Albertazzi Gonçalves Jr., Editor(s)

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