Case Study · 01 — Vision

Every part, inspected. Every parameter, learned.

An example of how machine learning closes the loop between inspection and production — turning a quality problem into a self-correcting system.

Concept demonstration Real-time defect detection Closed-loop control

01 — The status quo

Manual inspection breaks at the speed of a modern line.

1 in 50
defects missed by humans
12s
avg per visual inspection
±18%
variance, operator to operator

02 — The system

A closed loop, not a checklist.

Edge inference 8 ms
Loop latency < 200 ms
No cloud dependency

03 — The line

Every part. Every second. Every defect.

Inspected 0
Defects 0
Pass rate 100.0%
Throughput 2,400 / hr

04 — The feedback

The system noticed. The factory listened.

Anneal temp 412°F
Defect rate 2.0%
Adjustment cycle 4 min

05 — The result

−80%
Defects escaped to customers
+9%
Yield, line-wide
24/7
Inspection, no fatigue

Higher yield. Less waste. Every part better than the last.

A defective part used to leave the factory. Now it never starts.

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Foresight