Splitting Engine Checks by Station Instead of by Part
An engine is assembled from valves, oil seals, the flex plate, block, head, camshaft and pistons, and the block and head are large and varied. Four questions have to be judged on the line: is the part seated, is anything missing, is anything wrong, is the model correct. The targets are many and scattered, and several engine models run on the same line. This project deploys 2D vision by station, splits the check items to the nearest station, and runs a 40-second cycle with accuracy no lower than 99 percent.
One station seeing a whole engine is unrealistic
The check points follow the assembly flow: valves in the head, oil seals at specific bores, the flex plate at the end. Seeing the whole engine at one station would take an unrealistic number of cameras, or a field so wide that small parts could not be judged. So the approach is inverted: not single-point full inspection, but check items split by station, each station carrying the few items near it.
After the split, each station has few, concentrated capture points, so imaging and judgment take less time and cycle pressure spreads across the line.
The four verdicts rest on different evidence
Seated looks at installation depth and contact, missing looks at whether the target exists, wrong looks at whether the fitted part is the one specified, and model error looks at the identity of the whole unit. The criteria differ and have to be defined separately in the recipe: one capture point may carry both a seated and a missing verdict, but the thresholds cannot be mixed. Treating all four as a presence question is the most common failure in schemes of this type.
Block and head do not need whole-part imaging
Large parts can still be checked with 2D vision if they are not treated as one image. On a block or head the items usually sit at specific mounting locations, seal bores or valve faces, and splitting them into several capture points covers them. Each point then has a small field, a short working distance and high pixel density, so small parts are judged clearly. The price is many points, which the station split has to spread out.
Model recipes: changeover cannot rely on memory
With several engine models on one line, the biggest risk is not a weak verdict but a wrong standard: judging a B-model part against A-model benchmarks sends the whole batch to rework. Recipes are maintained per engine model, and at changeover the assembly system sends the model signal and the vision system loads the matching recipe. That makes changeover a system action rather than a human one.
Below the confidence threshold, a person decides
AI judgment uses the standard image and feature parameters in the recipe as its baseline and outputs a confidence from shape matching and feature comparison. Samples below the threshold go to manual recheck, so the algorithm does not have to force a conclusion on borderline samples and people handle only the few doubtful ones.
Cycle and accuracy are not separate accounts
The cycle is 40 seconds and accuracy is no lower than 99 percent, subject to site acceptance results. The cycle rests on two things: items split by station and imaged nearby, so each station's capture and judgment are short; and cameras triggered by the line cycle, with the verdict sent to alarm and blocking mechanisms within the cycle. An abnormal unit is blocked at its station, confirmed by an operator, then released or reworked.
Questions on a station-split scheme
How are small parts like valves and seals judged for seating?
By imaging the mounting location and comparing shape and position features, with camera resolution matched to that field.
Does a new component mean a new system?
No. Items and capture points are added inside an existing recipe, and layout and recipe structure are reused.
What distinguishes a model error from a wrong part?
A model error is the identity of the whole unit being wrong; a wrong part is the fitted component not being the one specified. They are defined separately.