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Illumination Sensor for Reflection-Based Characterisation of Technical Surfaces.

Tim Sliti1, Nils F Melchert1, Philipp Middendorf1

  • 1Institute of Measurement and Automatic Control, Stiftung Gottfried Wilhelm Leibniz Universität Hannover, An der Universität 1, D-30823 Garbsen, Germany.

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Summary
This summary is machine-generated.

A new sensor rapidly assesses turbine blade surface conditions using light reflection, aiding maintenance and quality checks. It shows promise in identifying areas needing detailed roughness analysis, complementing traditional methods.

Keywords:
BRDFreflectanceroughnesssurface measurement

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Area of Science:

  • Materials Science
  • Optical Engineering
  • Aerospace Engineering

Background:

  • Component functionality and lifespan depend heavily on technical surface condition.
  • Turbine blade surface roughness significantly impacts aero-engine performance.
  • Traditional roughness measurements are time-consuming for wide-area assessments.

Purpose of the Study:

  • To present an LED- and camera-based illumination sensor for rapid, wide-area characterization of turbine blade surfaces.
  • To investigate the relationship between reflectance parameters derived from the sensor and traditional surface roughness parameters.
  • To evaluate the sensor's potential for surface condition triage in maintenance, repair, and overhaul (MRO) and quality assurance (QA) contexts.

Main Methods:

  • Acquisition of multi-illumination image data using the developed sensor.
  • Per-pixel fitting of an anisotropic bidirectional reflectance distribution function (BRDF) model to obtain reflectance parameters.
  • Independent measurement of surface roughness parameters (Sa, Sq, Sz, Ssk, Sku) using confocal laser scanning microscopy as reference data.
  • Qualitative investigation of relationships between BRDF parameters and roughness characteristics on turbine blades with contrasting surface conditions.

Main Results:

  • Weak correlations were observed between BRDF parameters and height-based roughness parameters (Sa, Sq, Sz).
  • Clearer trends were found between BRDF parameters and surface distribution parameters (Ssk, Sku).
  • Good qualitative agreement was noted between directional BRDF parameters and surface texture orientation.

Conclusions:

  • The developed illumination sensor offers a complementary, reflectance-based approach for surface condition assessment.
  • The sensor can effectively highlight regions requiring more detailed roughness measurements, aiding MRO and QA processes.
  • Future work will involve quantitative analysis and application to diverse component geometries.