• Medientyp: E-Artikel; Sonstige Veröffentlichung
  • Titel: Development of a compact low coherence interferometer based on GPGPU for fast microscopic surface measurement on turbine blades
  • Beteiligte: Li, Yinan [VerfasserIn]; Kästner, Markus [VerfasserIn]; Reithmeier, Eduard [VerfasserIn]; Lehmann, Peter [VerfasserIn]; Osten, Wolfgang [VerfasserIn]; Albertazzi Gonçalves, Armando, Jr. [VerfasserIn]
  • Erschienen: Bellingham, Wash. : SPIE, 2015
  • Erschienen in: Optical Measurement Systems for Industrial Inspection IX : 22-25 June 2015, Munich, Germany ; Proceedings of SPIE 9525 (2015)
  • Ausgabe: published Version
  • Sprache: Englisch
  • DOI: https://doi.org/10.15488/1752; https://doi.org/10.1117/12.2184749
  • ISBN: 978-1-62841-685-5
  • ISSN: 0277-786X
  • Schlagwörter: CUDA ; Optical measurement systems ; Wave interference ; Non-contact measurement system ; Optical testing ; Vertical scanning interferometries ; Program processors ; Compute Unified Device Architecture(CUDA) ; Surface measurement ; Mathematical transformations ; Low coherence interferometers ; Computer graphics equipment ; Algorithms ; Konferenzschrift ; Interferometer ; Turbines ; Optical data processing ; Turbomachine blades ; Non-contact measurement systems ; Metadata ; Optical variables measurement ; Computer graphics ; Interferometry ; Turbine components ; [...]
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  • Beschreibung: Vertical scanning interferometry (VSI) techniques are widely used to profile microscopic surface structures of industrial products. This paper introduces a high-precision fast optical measurement system with an optimized small sensor head for the measurement of precision surfaces on a turbine blade or blisks (blade integrated discs). The non-contact measurement system is based on a low coherence interferometer (LCI), which is capable of fast profiling of 3D sample surface with a nanometer resolution and has a larger measurement range compared to conventional microscopes. This results in a large amount of sampled data and a high computational time for the evaluation of the data. For this reason, the used evaluation algorithm in this paper is accelerated by the Compute Unified Device Architecture (CUDA) technology, which allows parallel evaluation of the data stack on independent cores of a General Purpose Graphics Processing Unit (GPGPU). As a result, the GPU-based optimized algorithm is compared with the original CPU-based single-threaded algorithm to show the approximate 60x speedup of computing the Hilbert Transformation, which is used to find the depth position in the correlogram of each pixel of the sampled data. The main advantage of the GPU computing for the evaluation algorithm of the LCI is that it can reduce the time-consuming data evaluation process and further accelerates the whole measurement. © 2015 SPIE. ; DFG/SFB/871
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