How to reduce false rejects without missing defects

What a false reject is, and why it matters

A false reject is a good product that the station rejects. Its opposite is a missed defect: a defective product that passes. The false reject rate is counted against the good products inspected; the missed defect rate against the defective products inspected. They are reported together, because either can be lowered by raising the other.

False rejects cost more than the product in the bin. Operators stop trusting the station, and sooner or later someone switches it off or loosens a limit, and real defects get through.

Find the cause before touching the limit

Start with the images. Save the images of rejected products, let your quality team split them into real defects and good products, and group the good ones by what the station saw: glare, a part out of position, blur, normal variation, or nothing visible at all. That last group often points to reject tracking. The PLC and reject integration guide covers tracking and confirmation.

The drawing shows why the limit comes last. When good and defective results overlap, moving the limit only swaps one error for the other. Better imaging and samples pull the groups apart.

Imaging first: lighting, presentation, trigger, focus

Most false rejects start with an image of a good product that differs from the setup images. Four things decide how repeatable the image is.

• Lighting. The station light should be the only light that matters. Daylight, overhead lights and nearby stations change the image through the day; a shroud, strobing, or a lens filter matched to the light reduce that (NI practical guide to machine vision lighting). The geometry must suit the surface, because glare on shiny or curved parts causes many false calls. The machine vision lighting guide explains which light shows which defect.
• Presentation. Each product should reach the camera at the same position, angle and distance. Worn guides, touching products, tilted parts and speed changes all alter the view.
• Trigger. The sensor must see every product once, with the whole part in view. Late or double triggers give images of half a product, which fail.
• Focus and blur. Parts at different heights, a turned lens ring, vibration or an exposure too long for the speed soften edges. A soft edge can look like a defect, or hide one.

Samples: good variation and a borderline library

A station set up on a few perfect parts will reject normal ones. The good-sample set should cover what passes today: batches, shifts, moulds or cavities, print shades, colours, suppliers and every variant at the inspection point. Teach references from representative parts, not the best part on the bench. The sample preparation guide explains how to build and label the set.

Keep a borderline library: parts that are just acceptable and just rejectable, each with your quality team's decision, a photo and the reason. It settles arguments on the line, trains operators, and keeps deep-learning labels consistent. The rule-based or deep learning guide compares the two.

Limits: the trade-off, and how to set and record them

Every decision compares a result with a limit, measured on the good, defect and borderline sets. It is the trade-off statisticians describe for any test: making one kind of error less likely makes the other more likely, unless the measurement itself improves (NIST/SEMATECH e-Handbook, statistical tests). Where the limit sits is a quality decision, not a software setting.

• Set limits per defect type, not one overall sensitivity. A wrong code or missing seal justifies more false rejects than a cosmetic mark.
• Consider a review band between clear pass and clear fail, diverting borderline products to a manual check, if the line allows it.
• Record every limit: the value, the reason, the samples used, who approved it and when. Changes follow the same approval, and operator access to limits is restricted.

Verification: re-test with a fixed sample set

Keep a fixed verification set of good, defective and borderline samples that is not used for setup or training, and run it before and after every change. The figures match project acceptance:

• False reject rate: good products rejected, divided by good products inspected, at production speed.
• Missed defect rate: defective products passed, divided by defective products fed at known positions.

Record the sample set, speed and duration with each figure. Also run the same parts several times: a part that passes once and fails the next time shows poor repeatability, the question a gauge study asks of any measuring system (NIST/SEMATECH e-Handbook, gauge studies). A small run with no misses shows detection still works on those samples; it does not prove the station misses nothing in long-term production. See how we deliver a project for how these figures are written into FAT and SAT.

Drift: why false rejects creep back

• Lighting ageing: output falls over the light's life; images slowly darken.
• Dirty optics: dust, oil mist and splash build a film on the lens or cover glass.
• Mechanical movement: loose fixings, a camera knocked during cleaning, guides moved at changeover.
• New materials or suppliers: a new label stock, resin or ink changes gloss or colour.

Reference checks catch drift early. At agreed moments, such as shift start, after cleaning and after changeover, run a few known good and known defective reference samples and compare result and image with the stored reference. Plot the share of good products rejected per shift or batch; a proportions chart shows when it moves beyond normal variation (NIST/SEMATECH e-Handbook, proportions control charts). Then fix the cause and re-run the verification set, rather than retuning limits on the line. On stations we deliver, who runs these checks is written into the support plan.

Diagnostic table

Start from what you see on the line. These are common causes, not the only ones.