At a glance
- A part change is a software change: SkillReal defines what to inspect in software on standard cameras already mounted on the existing line.
- Per SkillReal, pre-trained large AI models are ready on day 1, with no part-specific AI training and no hundreds of good or bad parts required.
- Per SkillReal, a "deep lid" inspection covered 240 spot welds in top view, 148 in bottom view and 31 at a corner close-up.
- This guide gives prerequisites, numbered steps with expected outcomes, and the changeover mistakes that delay go-live.
Skillreal
Published:
Inline inspection, which means inspection carried out on the production line where the part is made, can go live after a part change as soon as the new part definition is loaded and verified at the station. The inspection plan lives in software, and the cameras stay where they are on the line. Per SkillReal, its pre-trained large AI models are ready on day 1, so a changed part needs no part-specific AI training and no collection of hundreds of good or bad parts before inspection resumes. Its AI Pipeline is a SkillReal feature for adding new parts to inspection on the production line very quickly. Per SkillReal, legacy robot and vision systems need 4–6 week re-teach cycles when parts change, and this 2026 guide walks through how to avoid that gap. It covers the prerequisites, the step sequence, the expected outcome of each step and the common changeover mistakes.
1. Scope the part change before touching the line
Before touching the line, scope the part change against the new CAD revision and classify each change. On a rack or cabinet variant, most changes are to holes, fasteners, welds and critical dimensions, and those are defined in software. Changes to the part's size or to how it sits on the line need a closer look before go-live.
| Attribute | What changes | Why it affects go-live |
|---|---|---|
| Feature set | Holes, welds, fasteners or critical dimensions added, moved or deleted | Redefined in software against the new CAD file; no physical work |
| Part envelope | The frame or enclosure grows in height, width or depth | Check that the cameras already on the line still see every changed feature |
| Part presentation | The part's orientation or position on the line changes | Confirm coverage on the real line before release |
| Line interface | Station sequencing and pass/fail handling | Re-verify these even when the geometry is unchanged |
SkillReal's AI Pipeline handles the changes to the feature set. The envelope and presentation changes decide whether any work on the line is needed at all.
2. Reuse the existing line layout and cycle time
Reuse the existing line layout: mount standard cameras directly on the existing production line, without building a dedicated inspection cell or ordering a new, complex measuring machine. A plant asked to add capacity fast cannot wait for new fixtures, a new robot or a new enclosure, so the inspection hardware has to adapt to the line, not the other way round. SkillReal describes its system as clip-on: it attaches to an existing production line and takes no extra floor space.
| Do this | Watch out for, and how to handle it |
|---|---|
| Mount cameras on the existing stations | Blocked operator access or load/unload clearance; walk the sightlines before installing |
| Keep inspection in-cycle, meaning it finishes inside the station's cycle time | Inspection eating into station time; check it against the measured station cycle before committing |
| Keep the current part handling on the line | Unplanned changes to the station; re-check against the CAD model at changeover |
Expected outcome: no station moved, no extra floor space used, and station cycle time unchanged after the change.
3. Update the inspection definition against the new CAD file
Update the inspection definition, which is the software list of which features are checked, against the released CAD revision. For rack production, SkillReal describes the change as technologically small: different parts, a different CAD file, the same principle. Engineers define in software what to check. Per SkillReal, its models are "trained on over 35,000 real-world manufacturing variations and enriched with millions of augmentations," so this step is configuration work rather than collecting and labelling a dataset of good and bad parts.
Expected outcome: the inspection definition covers every changed feature (holes, welds, fasteners and critical dimensions) on the new revision.
| Do this | Watch out for, and how to handle it |
|---|---|
| Define features from the released revision in the PLM system of record | A marked-up drawing can lag behind the model; confirm the revision before configuring |
| Extend coverage to features next to the change | Nearby geometry can shift unnoticed; include it rather than reopening the definition later |
| Re-check tolerances feature by feature | Tolerances carried over from the previous revision can hide a real deviation |
4. Validate assembly verification against known-good and known-bad units
Before the updated inspection governs production, validate it with assembly verification, which confirms that the parts on the line match the design, on a sample set of known-good and known-bad units. Start the bad set with the defect types the end customer has found before.
How to run the validation pass
- Pull physical examples of the problems recorded in returns, rework and installation findings, such as missing or misplaced fasteners, faulty welds and out-of-position holes.
- Run each unit through the station in-cycle, the way production parts will run.
- Check each result against a manual inspection of the same part.
- Run the known-good units more than once to confirm the station does not reject good parts and stall the line.
Expected outcome: the seeded defects are flagged and known-good units pass repeatably. According to SkillReal, its systems deliver dimensional accuracy to 0.05 mm at greater than 99.7% confidence, which is the figure to test your sample set against.
5. Hand over to production verification in stages
Hand inline inspection over to production verification in stages, not all at once. Until the results have been checked against the existing inspection on the same parts, keep that inspection in place and review every disagreement.
- Compare before relying. Run the new inspection alongside the current one and trace every difference to its cause. Expected outcome: no unexplained differences.
- Deployment phase. How the line responds depends on the plant. As SkillReal describes the deployment phase, the line stops on a deviation until an inspector physically checks the part.
- Fully in place. The ideal, once the system is fully in place and depending on the plant, is that no one has to handle the line. A robot moves the part to a separate pile that goes back into production, and SkillReal replaces the manual inspector completely. Review the aggregated inspection data and trends to see what is drifting.
Frequently Asked Questions
How fast can a changed part go live on the line?
A changed part can go live once its new definition is loaded and verified at the station, with no re-teach cycle. SkillReal's pre-trained large AI models are ready on day 1, so there is no part-specific training. The AI Pipeline then adds the revised part to inspection: a revised CAD file, an updated feature list, and the same cameras already mounted on the line. SkillReal states no fixed duration, so the time depends on the plant's scope and its validation.
What do I need in hand before starting a part change?
Before starting, you need the released CAD model of the new revision, the list of features that must be verified on it, and confirmation that the existing cameras still see those features. Engineers then define in software what to check, without ordering a new measuring machine. SkillReal supports bi-directional integration with Siemens Xcelerator (Process Simulate and Teamcenter), so setup and change management can be driven from the product lifecycle data the engineering team already maintains.
Does a part change require new floor space, robots, or a cell rebuild?
A part change keeps the existing footprint, because SkillReal fits onto the existing production line and takes no extra floor space, while inspection systems usually take a lot of space. The same technology has been proven in other industries. In SkillReal's case study with a leading Tier 1, 10 systems at one plant reached 100% automated inspection with direct PLC integration, with no new robots and no added floor space. In that case, hardware was installed during off-hours with no impact to production. The hardware is off-the-shelf industrial cameras and a line-side PC.
How do I verify coverage is correct after the change?
Confirm that every feature on the new revision is inspected in-cycle before releasing the line. As an example of what a camera-based setup can cover, SkillReal reports that in its inspection of a "deep lid" part, two cameras with 12 mm lenses inspected 240 spot welds in the top view, 148 in the bottom view, and 31 in a close-up view of a corner.
What happens on the line when a deviation is found?
The response depends on the plant. During deployment, the line stops until an inspector physically checks the part. The ideal, once the system is fully in place, is that no one has to handle the line: a robot moves the part to a separate pile that goes back into production. Aggregated data and trends come with it, so engineering sees what is drifting instead of only a good or not-good call.
Where has this capability been proven on data-center structures?
SkillReal states that its inline inspection "has already been proven on structural elements supplied to one of the world's largest data center and logistics operators." It bridges the gap between digital design and physical production directly on the line, enabling real-time inspection of holes, welds, fasteners and critical dimensions as parts are manufactured. For rack and cabinet production, SkillReal calls the change technologically small: different parts, a different CAD file, the same principle. SkillReal's Magazine article "The Data Center Boom's Unexpected Winners" covers its data-center work.
About this article
Skillreal publishes this article under its own name and is responsible for its accuracy. Articles are researched and drafted with AI assistance and approved by Skillreal before publication; publication and update dates reflect substantive edits, not automated refreshes. Last updated: 2026-11-06