Find out why defects happen.
Root-Cause AI watches every defect type for spikes and lines them up against machine events, tool changes, shifts and material batches. You get a ranked list of likely causes with the evidence attached.
Likely causes, ranked
Spike detection
Learns the normal rate of each defect type per line and flags real rises, not noise.
Machine events
Tool changes, die swaps, maintenance, setpoint changes and stops pulled from your PLC or MES.
Material batches
Coil, resin, film and component lots linked to the parts they went into.
Shifts and changeovers
Handovers and product changes marked on every chart.
Evidence attached
Each finding links to the defect images and timing behind it, ready for an 8D report.
Action tracking
Assign the suggested check, record the fix, and see if the rate came back down.
From a spike to a fix.
Root-Cause AI at a glance
| Event sources | PLC tags over OPC UA, MES events over REST, CSV upload for batch records |
|---|---|
| Factors | Machine, tool, die, mold cavity, shift, operator team, material lot, supplier |
| Output | Ranked causes with confidence, evidence images and timeline |
| Reports | 8D and CAPA starter report in PDF |
| Available on | 40× and 100× plans |
Asked by quality teams
Does it need our MES?
No. It works with PLC tags alone, and gets better with batch and maintenance records from MES or spreadsheets.
Is it a black box?
No. Every suggestion shows the events, timing and images it is based on, so your engineers can check it.
Does it tag individual operators?
No. It works at the level of shifts and teams. It is a tool for finding process causes, not for monitoring people.
Turn the knob to your line speed.
A 5% manual sample checks 5,760 of them. NanoQuantumAI checks all 115,200, and keeps a picture of each one.