PDCA For Vision Pick Rate
Apply PDCA to a robot vision tuning problem with a controlled change and evidence review.
A robot vision cell misses dark bottles during afternoon glare, and multiple settings are being changed at once. PDCA turns tuning into a controlled learning loop: Plan, Do, Check, Act. Not quite: changing exposure, threshold, and light angle together may improve one metric briefly, but it destroys learning. If false rejects rise, nobody can tell which variable caused the tradeoff. Plan Hypothesis: backlight angle is causing glare on dark bottles. Metric: pick success and false-reject rate across 300 bottles in the glare window. A good plan names the suspected cause, the change, the sample, and the decision metric before touching…
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