Dehnungsmessung von dünnen Naturfasern mit Laser Speckles

Translated title of the contribution: Strain Measurement Of Thin Natural Fibers Using Laser Speckles

Ricardo Gridling

Research output: ThesisMaster's / Diploma thesis

Abstract

Thin natural fibres are very versatile components with outstanding mechanical properties. Therefore, it is of great interest to obtain their mechanical characteristics. This thesis proposes a simple and cost-efficient method for single fibre tensile testing. Due to the small cross section of natural fibres, only contactless strain measurements are possible. The tensile testing device based on the Lorentz force principle is realized with a brushless DC motor drive. This enables a high dynamic range with superior accuracy. For the necessary calibration in the millinewton range, a method based on a precision balance is shown. Experiments confirm that this working principle is very promising, although special equipment is needed for high accuracy measurements. This work uses laser speckles for contactless strain evaluation. Speckles are random interference patterns that occur on rough surfaces when illuminated by sufficiently coherent light. Surface movement or deformation leads to changes in the speckle pattern. By tracking these changes, the movement or deformation can be estimated quantitatively. Application of a cross spectral density estimator allows the estimation of strain based on subpixel displacements of the speckle signal captured by the camera used. It is shown that this application is also in need for special equipment and in-depth consideration of wave-optics and signal theory are indispensable for high precision measurements.
Translated title of the contributionStrain Measurement Of Thin Natural Fibers Using Laser Speckles
Original languageGerman (Austria)
Supervisors/Reviewers
  • Zagar, Bernhard, Supervisor
  • Spaett, Alexander, Co-supervisor
Publication statusPublished - Mar 2022

Fields of science

  • 202012 Electrical measurement technology
  • 202024 Laser technology
  • 102003 Image processing
  • 202027 Mechatronics
  • 202037 Signal processing
  • 103021 Optics

JKU Focus areas

  • Digital Transformation

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