Speckle pattern characterisation for high resolution digital image correlation Mr George Crammond September 8th 2011 Supervisors: S. W. Boyd and J. M. Dulieu-Barton Overview & Motivation Current limitations Speckle pattern analysis Numerical image manipulation Experimental validation Conclusion Digital image correlation LaVision Digital Image Correlation system uses a cross-correlation algorithm to track the movement of a stochastic speckle pattern on the specimen surface from a sequence of recorded images From the algorithm, deformation vectors of the speckle pattern are calculated and the strains within the material can be evaluated Reference image t Deformed image t + dt Displacement vector Deformation vector field Strain field Motivation Image correlation used under high magnification to try and analyse strains within adhesively bonded joints Quality of data produced relatively poor Need to understand the error sources in the calculations to improve data Complex strain distributions Strain / % High noise εxx strain in joint loaded at 17kN Spurious data DIC Error sources Lighting Speckle pattern Correlation algorithm Error Specimen surface Applied strain Subset size Optics DIC Error sources Speckle pattern histogram Speckle size Lighting Speckle pattern Speckle distribution Number of speckles Correlation algorithm Error Specimen surface Applied strain Subset size Optics Speckle pattern analysis Important to consider the speckle pattern due to the physical trade-offs which exist when conducting digital image correlation Camera resolution Pattern uniqueness No. pixels per speckle No. speckles per subset Strain accuracy Resolution of data (subset size) Speckle pattern analysis Physical properties of patterns and the influence on measurement errors investigated 6.94mm Testing required to determine the suitability of current speckle patterns under increased magnification. Spraycan 296 pixel/mm Spraycan 296 pixel/mm Airbrush 296 pixel/mm Spraycan 705 pixel/mm Airbrush 705 pixel/mm 2.89mm 8.28mm Spraycan 705 pixel/mm 3.45mm Image processing methodology Morphological approach used to analyse the patterns based upon the shape, size and distribution of speckles in the pattern Computer vision techniques required to identify speckles Laplacian of Gaussian method utilised to provide edge detection of speckles in the image Alpha-shape image reconstruction also utilised to repair open contours created from the edge detection Image processing methodology 1. Raw image 2. Apply Laplacian of Gaussian edge detection method 3. Repair open contours using alpha shape image reconstruction 4. Produce binary image from the detected edges Pattern evaluation Pattern type Spray can Airbrush Background colour Speckle colour A X Black White B X White Black C X Black White D X White Black 296 pixels/mm 705 pixels/mm Four different pattern types investigated, altering application method and pattern colour Patterns tested under two levels of magnification, 296 & 705 pixels / mm Pattern evaluation Pattern type Spray can Airbrush Background colour Speckle colour A X Black White B X White Black C X Black White D X White Black 705 pixels/mm 296 pixels/mm A B C D 80 60 40 20 0 0 100 200 300 100 Cumulative percent / % Cumulative percent / % 100 A B C D 80 60 40 20 0 0 Speckle size / pixels 100 200 Speckle size / pixels At lower magnification very mixed responses observed As magnification increases, differences between patterns become greater Airbrush patterns show more even distribution of speckle sizes 300 Numerical image manipulation A known displacement field was imposed by manipulating the speckle image in MatLab in the image Fourier domain Displacement of stretched images calculated in LaVision DaVis software Deviation of the imposed and measured strain fields calculated Numerical image manipulation 705 pixels/mm 296 pixels/mm 0.01 A B C D 0.008 0.006 SD SD 0.008 0.01 0.004 0.002 0 0 A B C D 0.006 0.004 0.002 0.2 0.4 0.6 Strain % 0.8 1 0 0 0.2 0.4 0.6 0.8 1 Strain % Little difference seen between patterns at lower magnification although distributions different Under increased magnification, spray can patterns clearly show greater error than the airbrush Pattern with a white background also seen to exhibit lower error than those with a black background Numerical image manipulation 705 pixels/mm 705 pixels/mm A B C D 80 60 SD Cumulative percent / % 100 40 0.008 A B C D 0.006 0.004 0.002 20 0 0 0.01 100 200 Speckle size / pixels 300 0 0 0.2 0.4 0.6 0.8 1 Strain % Trends at the higher magnification compliment the distribution analysis conducted earlier which identified a difference between the pattern properties Having a pattern dominated by a large number of small speckles appears to be a sub-optimal solution Speckle size study Suspected that the reduced measurement errors for patterns with more even distributions is linked to the uniqueness of the speckles in the pattern A random pattern generator was created and different combinations of speckle size and density investigated x 10 3 -3 9 2.5 Speckle radius / pixels 8 7 2 6 1.5 5 1 4 3 0.5 2 4 6 8 10 12 14 Speckles per subset 16 18 0 Gradient in error values visible Identifies lower errors as speckle size and frequency increase Larger speckles have a greater level of uniqueness in size and shape, reducing uncertainty in measurement Image processing methodology Larger speckles also produce a correlation peak with a wider footprint This increases the number of points which define the peak Subpixel accuracy improved by this increase in points due to the 2D Gaussian curve fit used by LaVision 1 0.9 0.8 0.7 0.6 0.5 0.4 0.3 0.2 0.1 0 0 20 40 60 80 100 120 Image processing methodology Larger speckles also produce a correlation peak with a wider footprint This increases the number of points which define the peak Subpixel accuracy improved by this increase in points due to the 2D Gaussian curve fit used by LaVision 1 0.9 0.8 0.7 0.6 0.5 0.4 0.3 0.2 0.1 0 0 20 40 60 80 100 120 Experimental validation DIC performed over strain gauge on aluminium specimen at similar magnification levels to the numerical study Looking to validate the numerical study results and examine the practical issues with performing DIC at increased magnification Experimental validation 296 pixel/mm -3 02 1.5 x 10 705 pixel/mm -3 x 10 1.5 StrainAirbrush gauge0.02 - black Airbrush Airbrush - white 1 0.018 Airbrush Spraycan - black 2 - white SpraySpraycan can 0.016 18 16 Strain Airbrush gauge Airbrush Airbrush 1 Spray Spraycan can 2 Spraycan 1 Spray can 1 2 0.012 SD 12 1 Strain / 0.014 Strain / 14 01 08 0.01 0.5 0.008 06 0.006 0.5 04 0.004 0 0 500 02 0 0 1000 1500 0 0 2000 500 0.002 Load / N 1000 1500 Variability of results at low magnifications 0for both application methods 1 2 3 4 5 0 Strain % 2000 Load / N 1 2 3 Strain % 4 Difference between application techniques increases under higher magnification Supports earlier numerical deformation analysis 5 Conclusions Properties of speckle patterns extensively analysed using a morphological approach Different pattern types and application methods investigated Biggest differences seen at the high magnification level More even distributions of speckle sizes in the pattern appear to have a beneficial effect on the performance of the pattern Suspected to be due to an improvement in the subpixel accuracy from larger, more unique speckles Overall patterns created on a white background with an airbrush show the best pattern properties Any Questions?
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