Pattern Recognition of Rough Surfaces by Using Goniometric Scattered Light

• Autorzy: Geisler T. , Kolb A.

Identyfikatory

DOI

10.24425/118160

• Języki publikacji: EN

Abstrakty

EN

Nowadays, most techniques for evaluating rough metal surfaces are based on tactile or confocal measurement procedures. However, these technologies have disadvantages in respect to measuring speeds, resistance to vibration, impact and dust. In this paper we present a novel surface measurement approach, which uses the scattering light technology. Our approach enhances the state-of-the-art scattering light-based surface measurement methodology in both the detector setup and evaluation of the raw intensity values acquired by the scattered light device. The main goal in optimizing the measurement setup is to capture scattering parameters for rough surfaces in a range greater than 10 μm based on an enlarged detector array. Regarding the evaluation, we propose a pattern recognition approach which maps the reflection intensity I back to material structures and the tenpoint mean roughness Rz , the golden standard in tactile roughness characterization. Based on this approach, we are able to classify rough surface deviations like stripes using a simple but robust thresholding. In order to demonstrate the generality of our approach, we evaluate our approach using two rather different materials, i.e. brushed stainless steel and anodized aluminium.

Słowa kluczowe

EN

scattered light; rough surfaces; pattern recognition; stripe detection

• Wydawca: Komitet Metrologii i Aparatury Naukowej PAN
• Czasopismo: Metrology and Measurement Systems
• Rocznik: 2018
• Tom: Vol. 25, nr 1
• Strony: 33--46
• Opis fizyczny: Bibliogr. 15 poz., rys., tab., wykr.

Twórcy

autor

Geisler T.

• Universität Siegen, Computer Graphics and Multimedia Systems, 57076 Siegen, Germany

autor

Kolb A.

• Universität Siegen, Computer Graphics and Multimedia Systems, 57076 Siegen, Germany

Bibliografia

• [1] DIN Norm. (2014). Anodized products of wrought aluminium and wrought aluminium alloys. DIN Norm, DIN 17611:2011-11.
• [2] Volk, R. (2013). Rauheitsmessung Theorie und Praxis. Beuth Verlag.
• [3] Chondronasios, A. (2015). Investigation of surface defects for extruded aluminium profile using pattern recognition techniques.
• [4] Brodmann, R., Allgauer, R. (1989). Comparison of light scattering from rough surfaces with optical and mechanical profiles. Proc. Int. Congress on Optical Science and Engineering, International Society for Optics and Photonics, 111-118.
• [5] Brodmann, R., Thurn, G. (1986). Roughness measurement of ground, turned and shot- peened surfaces by the light scattering method. Wear, 109(1-4), 1-13.
• [6] Kaplonek, W., Nadolny, K. (2015). Laser methods based on an analysis of scattered light for automated, in-process inspection of machined surfaces: A review. Optik - International Journal for Light and Electron Optics, 126(20), 2764-2770.
• [7] Piln, L., De Chiffre, L. (2015). Validation of inline surface characterization by light scattering in robot assisted polishing . Technical report, Technical University of Denmark.
• [8] Norm VDA 2009 2010-07-00. Geometrische Produktspezifikation Oberflächenbeschaffenheit Winkelaufgelöste Streulichtmesstechnik Definition, Kenngrößen und Anwendung.
• [9] Yang, G., Hu, G., Guo, B. (2013). Multiple reflection cancellation using high order statistics adaptive fi for scattered triangulation laser displacement sensor. Advanced Materials Re111search, 774, 1613-1616.
• [10] Kaukoranta, T., Franti, P., Nevalainen, O. (1996). Empirical study on subjective quality evaluation of compressed images. Electronic Imaging: Science & Technology, International Society for Optics and Photonics, 88-99.
• [11] Brodmann, R. (2009). Geometrische Produktspezifikation Oberfächenbeschaffenheit Winkelaufgël oste Streulichtmesstech-Nik Definition Light Scattering Measurement Tech. Verband der Automobilindustrie E.V.
• [12] Brodmann, R. (2013). Robuste Rauheits und Formmessung für die Inline Prozesskontrolle.
• [13] Jelinek, T. (1997). Oberflächenbehandlung Aluminium. Leuze Verlag.
• [14] Brodmann, R. (2014). Datas Sheet OS 500-32 . OptoSurf GmbH.
• [15] Haralick, R. (2007). Textural features for image classification. IEEE Transactions on Systems, Man, and Cybernetics, SMC-3(6), 610-621.

Uwagi

PL

Opracowanie rekordu w ramach umowy 509/P-DUN/2018 ze środków MNiSW przeznaczonych na działalność upowszechniającą naukę (2018).