Presence/absence and assembly verification from 20 good images.

What do presence/absence and assembly verification check?

Presence/absence and assembly verification confirm that a part is built correctly: every component present, in the right place, the right label on the right unit. Presence/absence is the narrow question, is the screw, clip, gasket or label there or not. Assembly verification is the broader one, is the whole build correct, nothing missing, swapped or misplaced. Both are checks a quality or assembly engineer runs today by eye or with a fixture of sensors, and both are exactly the kind of appearance check a model can learn from good examples rather than a rule per feature.

The reason this matters is that a missing or wrong component is one of the most expensive escapes a line produces. It passes assembly, passes packing, and surfaces at the customer or in the field, where the cost of the fix is a large multiple of catching it at the station. A check that runs on every part, not a sample, is what closes that gap.

It also changes who can run the check. A learned good-build inspection does not need a rule written per fastener or a vision engineer to maintain it, so an assembly cell can add the check during a normal changeover rather than scheduling a separate vision project. The station inspects every unit and logs the result, which turns a manual spot-check into a per-part record the quality team can audit later.

How can a model verify an assembly from only 20 good images?

The model learns the correct build, not a list of every way it can go wrong. Shown about 20 good assemblies, it builds a picture of what a complete, correctly configured part looks like: the screw present, the clip seated, the label in position. At run time it scores each part against that learned good and flags anything that does not fit, a missing screw, an absent label, a swapped component. There is no need to collect a catalogue of every wrong build, which is fortunate, because the number of ways to misassemble a part is effectively open-ended while the correct build is singular. Learning the one good state is tractable; enumerating every bad one is not.

Adente Vision is an edge-AI visual inspection unit built by ADENTE Advanced Engineering Technologies, part of the Aden Group, sold through automation system integrators. Learning a correct assembly from about 20 good examples is a direct application of its good-parts-only training.

Why is "learn the good build" better than listing every wrong build?

Listing wrong builds fails for the same reason defect catalogues fail: you cannot collect what has not happened yet. A new misassembly appears without warning, a supplier substituting a slightly different clip, a label from the wrong batch, an operator seating a part backwards. A model trained only on known bad configurations misses the first occurrence of a new one. A model that learns the correct build flags any departure from it, so a never-seen misassembly is caught the same as a familiar one. It also collapses the data problem: about 20 good assemblies is a capture task of an afternoon, where staging hundreds of deliberately broken builds is a project on its own.

That is why presence and assembly checks fit the good-parts-only method so naturally. The correct configuration is the one thing on the line you always have plenty of, so the model trains on what is abundant and flags what is rare, which is exactly the right way round for a check meant to catch the occasional bad build.

Assembly checks, the good-example basis, and the line output

Read the table as a map from a check to how the unit learns it and what leaves the cell.

Each row is one check the model learns from the good build alone, and each result leaves as a signal the controller already understands.

How does the result reach the line, and how fast?

The unit turns each check into a signal your controller already understands. A correct build passes; a missing or wrong component becomes a fail with a reject reason, driven over one of five protocols, PROFINET, EtherNet/IP, Modbus TCP, EtherCAT or OPC UA, or over the 4 discrete outputs at 24V for a direct reject actuation. It triggers off an encoder pulse, a photoelectric sensor or a fixed interval, so capture is locked to the part. Where a component must be counted rather than merely present, the counting mode verifies the full set per part.

On speed, the measured field result is about 30 ms per part, with a 0.5 s per part catalog bound at 100+ parts per minute. Treat these as the envelope: the figure you can commit to for your own cycle needs an application-specific measurement on your parts and lighting. Every image is processed on the fanless Jetson-class board in the unit, so raw assembly imagery stays on the line.

What are the limits of vision-based assembly verification?

Vision verifies what the camera can see. A component hidden behind another, or a fastener torque that leaves no visible sign, is outside a single viewpoint's reach, so hidden checks stay with sensors or a second station. A single unit is a single viewpoint, so covering several faces of an assembly is a fixturing and layout question. And a borderline part, one whose confidence sits near the threshold, should route to a safe default and be checked rather than forced into a pass. Because the unit exposes per-part confidence, an integrator can set that operating point to the cell's real cost of a wrong pass, the same mechanism that recognises variants such as bumpers above 0.9 confidence in the field.

For the method behind learning from about 20 good images, see the sibling post on inspecting with 20 images, and for the full picture of few-shot and anomaly inspection, see the pillar guide.

The same unit runs presence, absence and assembly verification alongside the other checks on the applications page.