Defect detection across a 12-line production floor.
39%
Less downtime
96.6%
Defect detection
5 wks
To production
12
Lines covered
Where operations were breaking down.
Visual inspection was a manual handoff between shifts, and defects often surfaced downstream, at packing, or worse, at the customer site. The recall risk was rising alongside warranty cost, and quality engineers spent half their week chasing root cause instead of preventing recurrence.
Downstream maintenance compounded the problem. Unplanned line stoppages averaged 3.4 hours per week per line, with no central record of which faults preceded which failures. Operators had instincts, but no shared signal.
Leadership wanted a system that could flag both quality defects and equipment anomalies before they triggered a stoppage, without ripping out the existing MES or PLC tooling.
How Aelix Forge was deployed.
Computer-vision quality inspection
Camera arrays at four critical inspection points feed a defect-classification model trained on three years of historical reject samples. Confidence scores route ambiguous cases to a human reviewer.
Sensor telemetry ingestion
PLC and SCADA streams pipe into the operational data lake. Anomaly-detection models flag deviations in vibration, temperature, and cycle time before they cross failure thresholds.
Shift-level dashboards
Floor supervisors get real-time dashboards showing defect rates per line, top recurring root causes, and predictive maintenance windows. Mobile-responsive for walking the floor.
MES writeback + audit
Every detection writes back to the existing MES with full audit lineage, so quality and compliance reporting stays intact without parallel systems.
From source data to operational action.
Stage 01
Vision + telemetry
Cameras and PLC sensors stream raw signals continuously from every line.
Stage 02
Ingest + normalize
Edge gateways unify camera frames, sensor packets, and shift logs into a single feature store.
Stage 03
Defect + anomaly models
Vision and time-series models score each frame and signal window in real time.
Stage 04
Alerts + writebacks
Floor supervisors get pushed alerts; MES records every detection with audit lineage.
Stage 01
Vision + telemetry
Cameras and PLC sensors stream raw signals continuously from every line.
Stage 02
Ingest + normalize
Edge gateways unify camera frames, sensor packets, and shift logs into a single feature store.
Stage 03
Defect + anomaly models
Vision and time-series models score each frame and signal window in real time.
Stage 04
Alerts + writebacks
Floor supervisors get pushed alerts; MES records every detection with audit lineage.
Operational gains after going live.
0%
Less downtime
Predictive alerts catch sensor anomalies before they cascade into line stoppages.
0.0%
Defect detection rate
Vision models outperform manual inspection on consistency across shifts.
0%
Fewer recalls
Caught upstream during inspection instead of downstream at the customer site.
5 wks
From kickoff to production
Integration with existing MES and PLC infrastructure required no replacements.
3.4 → 0.9
Hours/week of unplanned stoppage
Per line, averaged across the 12 lines covered in the initial rollout.
$1.8M
Annualized savings
Combined recall avoidance, scrap reduction, and uptime improvement in year one.
See what Aelix Forge would do for your operations.
Talk to our team about the bottlenecks slowing you down, and the system we'd deploy to remove them.