Control-module assembly for electronic equipment
Multi-model line, zero code, minutes to changeover.
A contract manufacturer of electronic equipment runs a high-mix control-module line with heavy text-content and component-placement variance. Visual Inspection AI Agent replaced brittle rule-based vision across multiple stations.
Anonymized electronics assembly line with multiple control module variants and Visual Inspection AI Agent overlay
- Industry
- Electronic equipment manufacturing
- Product
- Visual Inspection AI Agent
- Scope
- Multi-station, multi-variant
- Changeover
- Minutes per new variant
A traditional vision stack that could not keep up with the SKU mix.
The line runs many control-module variants back to back. The previous rule-based machine-vision setup needed weeks of engineering for every new SKU, and lighting drift caused cascading false positives on production runs.
Engineering bottleneck on new SKUs
Every new product variant required the vision specialist to rewrite templates, re-tune thresholds, and re-validate — the inspection step became the gate on how fast new SKUs could launch.
Fragile under lighting and material drift
Small changes in ambient lighting or surface finish produced false-reject spikes, eroding the operator trust that had taken months to build.
Text and label variance uncovered
Each variant had different text content, labels, and component layouts. Classical OCR combined with template matching could not reliably verify all three dimensions at once.
Changeover eating throughput
On a mixed-model line, the inspection station — not the assembly itself — was the real constraint on daily output.
Replace rule-based vision with a semantic AI Agent.
The Insightek engineering team deployed Visual Inspection AI Agent at the critical control-module stations. Line engineers ran the entire registration workflow themselves — no machine-vision specialist in the loop.
- 01
Register a baseline per variant
For each variant, line engineers photographed a handful of OK samples. The AI Agent auto-recognized the module structure, text regions, and key components — then a single operator confirmed the layout through a visual interface.
- 02
Integrate with the line PLC
The inference service was wired to the existing PLC trigger. On every captured frame, the agent produced an OK/NG verdict plus an annotated process image and a structured log entry for the MES.
- 03
Roll out to remaining stations
Once the first station was stable, station templates were replicated to the rest of the control-module line, and new SKUs were added by re-running the register-and-confirm workflow.
Three-step deployment workflow for the control-module line
No code was written during deployment. All registration and changeover was done by line engineers through the visual interface.
What the line changed — with disclosure.
These numbers come from this named customer deployment under NDA. Every figure below is published together with its test method and baseline so the claim is unambiguous.
| Metric | Value | Test method | Baseline |
|---|---|---|---|
| Engineering debug time | −95% | End-to-end time from blank line to first OK/NG on a new product variant | Customer's prior traditional machine-vision setup on the same line |
| Single-station throughput | +50% | Takt-time measurements post-deployment vs. pre-deployment baseline | Manual visual inspection at the same station |
| Programming lines of code | 0 | Engineer timesheet during model registration (photo + confirm) | — |
| Product changeover time | Minutes | Stopwatch from new sample in hand to stable OK/NG | Traditional rule-based vision: hours to days |
Figures are from a single named customer deployment under NDA. Specific numbers vary by line, product mix, and lighting conditions. A full methodology report is available on request after NDA.
Run a similar rollout on your control-module line.
Book a 30-minute demo with the engineering team. We will walk through your line, your variant mix, and a realistic POC plan.