Metal detectors catch metal, X-ray catches dense materials, and neither one was ever built to catch everything that can end up in a food product. A fragment of HDPE tote, a sliver of wood from a pallet, a shard of glass from a broken gauge, or a piece of bone that splintered during processing can all pass straight through a metal detector without triggering anything, because none of them are metal. Catching the full range of foreign material categories at line speed, with a reject log that can actually support a recall investigation, is what a multi-material AI vision inspection setup looks like on a real line.
One Inspection Point, Every Material Category
iFactory AI trains a single vision model to catch metal, plastic, glass, wood, and bone fragments together, logging every reject with an image the team can actually review.
Why One Detection Method Was Never Enough
Every traditional inspection method was built around a specific physical property, which means its blind spot is simply whatever material does not share that property.
Metal Detectors Only See Metal
A detector tuned for ferrous and non-ferrous metal has no way to register a plastic, glass, wood, or bone fragment, regardless of size.
X-Ray Depends on Density Contrast
X-ray inspection catches dense contaminants well, but a low-density plastic fragment can sit below the contrast threshold against certain products.
Human Visual Checks Don't Scale to Line Speed
A person watching a fast-moving line can catch an obvious contaminant occasionally, but cannot sustain consistent attention across a full shift at production speed.
What Each Inspection Method Actually Catches
Understanding each method's actual coverage, rather than its marketed coverage, is what shows where the real gap sits.
| Method | Strong Against | Typical Gap |
|---|---|---|
| Metal Detector | Ferrous metal, larger non-ferrous fragments | Plastic, glass, wood, bone; small stainless fragments |
| X-Ray Inspection | Dense glass, bone, some metal | Low-density plastic against certain product densities |
| AI Vision Inspection | Surface-visible fragments across material types | Material fully embedded inside the product |
A layered approach, combining vision with whichever of metal detection or X-ray already fits the product, covers more of the real-world contamination range than any single method running alone.
See Where Your Current Setup Has a Material Gap
Bring your current inspection equipment list to a 30-minute session and identify which material categories aren't actually covered yet.
Building a Defensible Reject Log
A reject is only useful evidence if the record behind it can actually answer the questions an auditor or an investigator will ask.
Where Vision Inspection Fits on the Line
Placement matters as much as detection accuracy, since a camera positioned after the point where product is already sealed cannot prevent a reject from reaching a customer.
An Illustrative Comparison: Material Categories Caught
The figures below are a labeled illustrative composite, not a reported customer result, showing how coverage can change when vision inspection is layered onto an existing metal detector setup.
The plants that get the most value from vision inspection are the ones that treat it as a layer added to existing equipment, not a replacement for it. Metal detection and X-ray are still the right tools for what they catch well, and vision inspection is what closes the specific gap those methods were never designed to cover, which is why the strongest setups run all three together rather than debating which one to keep.
Frequently Asked Questions
Does this replace our existing metal detector or X-ray system?
No, vision inspection is designed to run alongside your existing equipment, covering the material categories metal detection and X-ray were never built to catch rather than duplicating what they already handle well. See how this layers onto your current setup in a short session.
Can one system really catch all five material types at once?
Yes, a single vision model can be trained on multiple material categories simultaneously, scanning for metal, plastic, glass, wood, and bone fragments in the same pass rather than requiring a separate inspection station per material type. Ask the iFactory AI team which materials matter most for your product.
What happens to a product once it's flagged?
It is diverted from the line before reaching final packaging, with the detection logged by timestamp, material category, and image, so the team can review the specific flagged item rather than guessing at what triggered the reject. Walk through divert logic for your own line layout in a demo.
How does the reject log hold up during an audit?
Each reject carries a timestamp, the active lot and work order, the material category, confidence level, and an image of the actual detection, which gives an auditor a specific, reviewable record rather than a generic pass or fail count. Review a sample reject log during a walkthrough.
Can the model be trained on materials specific to our plant?
Yes, the vision model is trained on the specific colors, shapes, and textures of materials actually present in your facility, such as the particular tote color or glove type your plant uses, rather than a generic material library. Discuss training data requirements for your facility with the support team.
Close the Gap Every Single-Method Detector Leaves Open
iFactory AI catches metal, plastic, glass, wood, and bone fragments in one inspection pass, with a reject log built for review. Book a walkthrough on your own line.
