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Tytuł artykułu

Free-form surface data registration and fusion. The case of roughness measurements of a convex surface

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Warianty tytułu
Języki publikacji
EN
Abstrakty
EN
Combining surface measurement data from individual measurements of surface fragments is an issue that has been recognized for flat surfaces. The connection takes place on the principle of making ‘overlap’ measurements according to a specific measurement strategy, and then the algorithm synthesizes the measurement data for the common part (data fusion). This paper presents a method of combining partial data into one larger set using image processing methods. The purpose of the analysis is to combine surface data of a more complex shape in terms of surface roughness and waviness. A successful attempt was made to combine surface measurement data located on a cylindrical surface - convex surface. A rotated table was designed and used for surface data acquisition. The datasets were acquired with the use of CCI 6000 (366μm - 366μm) with the assumed overlapping of at least 20%. The measurement datasets were first pre-processed: filling in non-measured points, levelling and form removing were applied. For such processed datasets, the common part was identified (data registration) and then the data fusion was performed. An example of stitching the surface datasets shows usefulness of the presented methodology.
Rocznik
Strony
323--333
Opis fizyczny
Bibliogr. 27 poz., rys., tab.
Twórcy
  • Koszalin University of Technology, Faculty of Mechanical Engineering, Racławicka 15-17, 75-620 Koszlin, Poland
Bibliografia
  • [1] Abdul-Rahman, H.S., et al. (2013). Freeform surface filtering using the lifting wavelet transform. Precis. Eng., 37(1), 187-202.
  • [2] Adamczak, S., Makieła, W. (2002). The simulation method for the determination of the measuring error of a curvilinear profile exemplified by a circle using a co-ordinate measuring machine. Metrol. Meas. Syst., 9(3), 291-302.
  • [3] Boyd, S., Vandenberghe, L. (2004). Convex optimization. Cambridge University Press.
  • [4] De Chiffre, L., et al. (2003). Surfaces in Precision Engineering, Microengineering and Nanotechnology. CIRP Annals - Manufacturing Technology, 52(2), 561-577.
  • [5] Esteban, J., et al. (2005). A review of data fusion models and architectures: towards engineering guidelines. Neural Comput. Appl., 14(4), 273-81.
  • [6] Fabio, R. (2003). From point cloud to surface: the modeling and visualization problem International Archives of Photogrammetry. Remote Sensing and Spatial Information Sciences, 34(5), W10.
  • [7] Gonzalez, R.C., et al. (2004). Digital image processing using MATLAB. Pearson Education India;.
  • [8] Grzesik, W., et al. (2007). Surface finish on hardened bearing steel parts produced by superhard and abrasive tools. International Journal of Machine Tool & Manufacture, (47), 255–262.
  • [9] He, W., et al. (2018). A robust and accurate automated registration method for turbine blade precision metrology. The International Journal of Advanced Manufacturing Technology, 97, 9-12, 3711-3721.
  • [10] Huang, J., et al. (2016). High-Precision registration of point clouds based on sphere feature constraints. Sensors, 17(1), 1-14.
  • [11] ISO 25178-2:2012: Geometrical Product Specifications (GPS) - Surface Texture: Areal - Part 2: Terms, Definitions and Surface Texture Parameters.
  • [12] Jiang, X., Whitehouse, D.J. (2012). Technological shifts in surface metrology. CIRP, 61(2), 815-836.
  • [13] Jiang, X., et al. (2010). Template matching of freeform surfaces based on orthogonal distance fitting for precision metrology. Meas. Sci. Technol., 21(4), 045101.
  • [14] Khaleghi, B., et al. (2013). Multisensor data fusion: a review of the state-of-the-art Inform.Fusion,14(1), 28-44.
  • [15] Kase, K., Tashiro, H. (1999). Method for combining partially measured data. US6611791B1.
  • [16] Lee, D.-H., et al. (2015). The five-degree of freedom stitching method of areal surface data for high precision and large area measurement. Proc. of the Institution of Mechanical Engineers, Part C: Journal of Mechanical Engineering Science, 229/11, 2066-2080.
  • [17] Ling, B.K., et al. (2017). Development of Data Registration and Fusion Methods for Measurement of Ultra-Precision Freeform Surfaces. Sensors, 17/5/1110, 1-15.
  • [18] Liu M.et al. (2016). A gaussian process data modelling and maximum likelihood data fusion method for multi-Sensor CMM measurement of freeform surfaces. Appl. Sci., 6/409, 1-22.
  • [19] Liu, M.Y., et al. (2017). A Gaussian process and image registration based stitching method for high dynamic range measurement of precision surfaces. Precision Engineering, 50, 99-106.
  • [20] Marinello, F., et al. (2007). Development and analysis of a software tool for stitching three-dimensional surface topography data sets. Meas. Sci. Technol., 18, 1404-1412.
  • [21] MountainsMap®, Digital Surf. http://www.digitalsurf.fr/en/mntkey.
  • [22] Müller, M., et al. (2007). Robust image registration for fusion. Information Fusion, 8(4), 347-353.
  • [23] Rasmussen, C.E., Nickisch, H. (2010). Gaussian processes for machine learning (GPML). Toolbox J. Mach. Learn. Res., 11, 3011-3015.
  • [24] Wang, J., et al. (2015). Review of the mathematical foundations of data fusion techniques in surface metrology. Surface Topography: Metrology and Properties, 3, 023001.
  • [25] Rasmussen, C.E., Williams, C.K. (2006). Gaussian processes for machine learning. The MIT Press.
  • [26] Zawada-Tomkiewicz, A., Tomkiewicz, D. (2013). Surface image enhancement and discrimination with the application of surface decomposition. Measurement Automation Monitoring, 11, 1174-1178.
  • [27] Zhang, Z. (1994). Iterative point matching for registration of free-form curves and surfaces. Int. J. Comput. Vision, 13(2), 119-52.
Uwagi
PL
Opracowanie rekordu w ramach umowy 509/P-DUN/2018 ze środków MNiSW przeznaczonych na działalność upowszechniającą naukę (2019).
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-14b4f7ed-2b14-4dad-806f-6142c6f2456c
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