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Image-based roughness estimation of laser cut edges with a convolutional neural network

Tatzel, Leonie; León, Fernando Puente

Abstract:
Laser cutting of metals is a complex process with many influencing factors. As some of them are subject to change, the cut quality needs to be checked regularly. This paper aims to estimate the roughness of cut edges based on RGB images instead of surface topography measurements.

We trained a convolutional neural network (CNN) on a broad database of images and corresponding roughness values. The CNN estimates the roughness well with a mean error of 3.6 µm. Sometimes it is more reliable than the surface measuring device because the RGB images are less prone to reflectivity problems than the measurements.

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Verlagsausgabe §
DOI: 10.5445/IR/1000127883
Veröffentlicht am 19.12.2020
Originalveröffentlichung
DOI: 10.1016/j.procir.2020.09.166
Cover der Publikation
Zugehörige Institution(en) am KIT Institut für Industrielle Informationstechnik (IIIT)
Publikationstyp Zeitschriftenaufsatz
Publikationsjahr 2020
Sprache Englisch
Identifikator ISSN: 2212-8271
KITopen-ID: 1000127883
Erschienen in Procedia CIRP
Verlag Elsevier
Band 94
Seiten 469–473
Bemerkung zur Veröffentlichung 11th CIRP Conference on Photonic Technologies, LANE 2020; Virtual, Online; ; 7 September 2020 through 10 September 2020
Schlagwörter laser cutting, cut edge quality, roughness, convolutional neural network
Nachgewiesen in Scopus
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