Inspection Moves From the End of the Line to the Station: Screw-Removal Cells Are Now Asked to Prove They Saw Clearly

For a decade, inspection in 3C manufacturing followed a clean division of labour: stations perform actions, inspection judges results. A screw-removal station took the screws out; cosmetic and assembly quality were handled downstream by AOI or by human visual check. That division was economical in an era of large batches, few models and stable yield — inspection equipment is expensive, utilisation must be high, and centralised placement spreads the cost.

In 2026 the line is being redrawn. What drives it is not inspection technology itself but three developments happening at once.

The first is that defects have become more time-sensitive. Previously a batch was completed and then inspected, and a problem could be caught and reworked as a batch. With batch sizes narrowing to a few hundred units, by the time downstream inspection finds the issue the material has already moved to the next process or shipped. Process engineering therefore asks for something more practical: a problem should be stopped at the station where it was created. For a removal station that means it can no longer report only how many screws it removed; it has to report the state of each screw before removal and whether the removal itself was abnormal.

The second is rising complexity of the object being disassembled. Foldable structures, stacked boards, dispensed adhesive reinforcement and irregular shielding covers mean that "where is the screw" is no longer a question a coordinate table can answer. Incoming tolerance, transport deformation and repair traces vary from batch to batch and change the actual position and surface condition of the screws. Open-loop removal on fixed coordinates fails at a low but persistent rate under such variation, and the failure mode is usually cam-out or a bit spinning free — neither raises an alarm, both quietly damage the workpiece.

The third is that AI models have become deployable. Two or three years ago, running a small model for single-station inference on an industrial PC required a dedicated GPU and a thermal solution, and the cost would not come down. Today mainstream edge compute platforms run lightweight segmentation and classification models in a fanless enclosure, and the incremental cost per station has fallen into a range a project budget can absorb. That is the real reason the shift is happening: not that the algorithms got smarter, but that compute became cheap enough to waste at every station.

Together these three turn process-level inspection from an optional bonus into a design premise. But what it brings is not simply one more camera.

The real change is that the station starts to carry an evidentiary responsibility. Once inspection moves to the station, the station becomes the first producer of quality data: the images, torque curves and timestamps it generates must bind to a specific product and be traceable back to an individual screw weeks later. That requirement reshapes the equipment in reverse. The camera can no longer be merely a sensor that helps find position; it must be a record source that retains, compares and can be retrieved. Torque data can no longer be a limit alarm; it must keep the full curve. The station can no longer expose a single result code; it needs traceable process data.

From the supply side, this shift changes what vendors must be able to do. Acceptance used to be about whether the screw comes out and at what cycle time. Increasingly the technical agreement states outright that the station must retain images, classify exceptions and exchange data with MES. Across its long project history in automatic fastening and combined drive-and-remove cells, Shenzhen Honred Technology has seen the questions around its small screw removal equipment change accordingly: what customers now ask is not axis speed but whether the camera mount leaves adjustment margin for a second pass, whether the light source can be changed without stopping the machine, and whether there is still room in the control cabinet for an edge computing box.

The role of vision has shifted too. On a station built around an automatic vision screw removal machine, vision used to be a locating tool: find the screw centre, send coordinates to motion control, done. The same camera is now asked to do a second job — capture one frame before and one after removal, judge whether the screw head is intact, whether there is debris in the recess, whether the threaded hole shows galling or shedding afterwards. The two jobs do not want the same image. Locating wants contrast and edge sharpness; judging wants uniformity and repeatability. One light position rarely satisfies both. The practical answer is to store two imaging parameter sets and switch them at different process steps with the same hardware, rather than sharing one set to save cost and then chasing thresholds during judgement.

What must accompany this is consolidation on the data side. Process-level inspection generates far more data than centralised inspection because it is produced per station, per product and per screw. If every station stores its own, within weeks the shop floor has a set of islands that cannot be aggregated. A workable architecture keeps only local cache and short-term retention at the station, reports structured results (verdict code, defect class, timestamp, product barcode) in real time, and uploads images on a sampling policy. Traceability is preserved without drowning the network and storage in images.

For projects with a wide specification spread, the logic needs one more layer. Across the M0.8 to M2.0 range, heads run from Phillips to Torx to hex socket, with clearly different recess depth and width, and a single model rarely stays stable over all of them. The pragmatic approach is to build models per head type and size group, give each group its own thresholds, and aggregate the verdicts into one report. This is also why the M0.8-M2.0 screw removal solution insists on tiered configuration: the tiers are not there to make the machine complicated but to give each group a defined boundary of imaging and judgement validity.

One caution: process-level inspection does not mean moving all inspection onto the stations. It has a clear boundary. It is suited to catching process defects — cam-out, head shear, missed screws, debris, position offset — and unsuited to replacing final inspection items that need large-area scanning or high magnification. Draw the boundary clearly and the investment does not turn into a set of poorly utilised cameras.

Seen from the industry level, the significance of this shift may lie less in inspection accuracy than in who owns the quality data. When every station can prove that it saw clearly and recorded what it saw, quality responsibility moves forward from the end of the line to the process, and the path from rework to accountability shortens. For a step long treated as auxiliary, such as screw removal, this is pressure — and the first real chance to enter the quality system on its own terms.