Part V · Practice

13. Speckling a specimen

Chapter 1 explained why a speckle pattern is necessary at all: a featureless surface has no unique texture to lock onto. This chapter is about what makes one pattern actually work well and another one fail quietly.

What a good pattern needs

• Random, not periodic. A repeating pattern - an evenly spaced grid of dots, a woven fabric texture - gives the correlation search more than one place that looks like an equally good match. The search can lock onto the wrong repeat of the pattern with high apparent confidence, which is a far worse failure than an honest low correlation score: it reports a plausible, wrong answer instead of admitting doubt.
• High local contrast. The correlation is only as good as the intensity gradient inside each subset. A washed-out, low-contrast pattern gives the search little to grip, which is exactly what a noise floor (Chapter 9) measures directly: weak local contrast against a given camera's own noise level produces a correspondingly coarse noise floor, however good the pattern looks to the eye.
• Feature size matched to the subset, not to the whole image. What matters is not how the pattern looks across the whole specimen, but how much distinct texture falls inside one subset. A pattern with speckles far larger than the subset gives each subset too little variation to be unique; a pattern with speckles far smaller can blur together at the imaging resolution actually being used. Chapter 14 covers choosing the subset itself; the two decisions have to be made together, not in either order alone.
• Genuine full-range contrast, without saturating. A pattern that is mostly pure black and pure white loses gradient information exactly where the camera clips - a saturated pixel carries no information about how much brighter or darker the true value was, so a pattern that clips throws away contrast it could otherwise have used.

How a pattern gets onto a specimen

Some specimens already carry enough natural texture to correlate well and need nothing applied at all - worth checking, with a real photograph and a real quality estimate, before assuming a pattern is necessary. Where one is needed, common methods include spray or airbrush application of a fine random speckle, and printing a computer-generated, non-periodic pattern onto a decal or stencil and transferring or applying it to the specimen.

Checking a pattern before committing to a whole test

The cheapest check is the one done before any load is applied at all: photograph the specimen as speckled, and look at the reliability figures a correlation of that reference image against itself, or against a first small step, actually produces. A live estimate of what a chosen subset radius can resolve against the pattern actually present is worth having on screen while those settings are still being chosen, precisely because it is far cheaper to re-speckle a specimen before a test than to discover a pattern was too weak only after the test is finished and cannot be repeated.

14. Choosing subset radius and grid step

Two numbers control almost every correlation: how large a patch of pixels each measurement point looks at, and how far apart the measurement points are spaced. Neither has a single correct value; both are trade-offs that depend on the pattern and on what is being measured.

Subset radius: robustness against locality

A larger subset contains more texture, which generally makes its correlation more robust and its noise floor finer (Chapter 9) - more pixels means more gradient energy to work with, and a steadier average against sensor noise. The cost is spatial: a large subset blurs the field, because a single reported point is really an average over a fairly wide patch of the specimen. Where the deformation changes sharply over a short distance - near a hole, at a crack tip, across a narrow gauge section - too large a subset averages the interesting detail away before it is ever reported.

A smaller subset can follow sharper local detail, but has less texture to work with, is more sensitive to noise, and needs a correspondingly finer speckle pattern to still contain enough unique contrast to correlate reliably at all.

Grid step: sampling density against cost

The grid step is simply how far apart the measurement points are placed. A finer step gives a denser field and a smoother-looking result, at a roughly proportional cost in computation. It does not, on its own, improve the quality of any individual point's measurement - that is entirely the subset's job - so a very fine grid step over an unreliable subset produces a dense field of unreliable answers, not a genuinely more precise one.

Working from the reliability figures, rather than guessing

The two settings are not really independent, and neither is right or wrong in isolation: the sensible way to choose them is to look at what the actual pattern, at the actual imaging resolution being used, can resolve at a candidate subset radius - the noise floor from Chapter 9, computed live against the settings under consideration - rather than picking a subset radius by habit or by copying a value from an unrelated test.

15. Lighting and imaging

Everything in Parts I through IV assumes the two photographs differ only by the deformation being studied. Lighting, focus, and camera settings that drift between reference and target introduce differences that have nothing to do with the specimen, and DIC cannot tell those apart from real movement.

Even, diffuse, and stable

Direct, hard lighting produces specular highlights - small, very bright patches that shift position as the specimen itself moves, even though the surface underneath is speckled normally. A highlight sliding across a subset between reference and target looks, to a correlation search, like a change in the pattern itself, and can corrupt an otherwise perfectly good measurement. Even, diffuse lighting, without a strong single source, avoids this.

Lighting also has to stay stable for the whole duration of a test, not merely at the moment each photograph is taken. A light that dims, flickers, or casts a moving shadow across the specimen over the course of a long test introduces intensity changes a correlation search has no way to separate from real deformation.

Fixed focus, fixed aperture, fixed everything that can auto-adjust

Any camera setting that can change automatically between shots - autofocus, auto-exposure, auto white balance - is a setting that can quietly change the image in a way indistinguishable, to the correlation, from real deformation. All of them should be fixed manually before a test begins and left untouched for its entire duration, including through however many load steps and however much time the test takes.

The camera itself has to hold still

Everything this manual has said about a stable coordinate frame (Chapter 4) assumes the camera did not move between the reference photograph and any target. A tripod or rigid mount that cannot shift, even slightly, over the course of a test is not a minor convenience - a camera that moves introduces a rigid displacement across the entire field that is real, in the sense that it genuinely happened, and completely uninteresting, in the sense that it has nothing to do with the specimen being studied, and no way to tell the two apart after the fact.