THE RESULT
What the data shows
The largest computed metric is 86 for K_Scatch; the smallest is 24.5 for Stains. Metric: median_contrast (Source luminosity range).
Contrast depends on image acquisition and is not a causal explanation of manufacturing faults.
| fault | records | median contrast | median luminosity index |
|---|---|---|---|
| Bumps | 402 | 35 | -0.1431 |
| Dirtiness | 55 | 28 | -0.0884 |
| K_Scatch | 391 | 86 | -0.1563 |
| Other_Faults | 673 | 28 | -0.1188 |
| Pastry | 158 | 44 | -0.179 |
| Stains | 72 | 24.5 | -0.00255 |
| Z_Scratch | 190 | 27 | -0.1646 |
THE METHOD
From source to answer
Derived image contrast and class-level median sensor summaries.
The Python source exposes this study’s transformations. The complete project download includes shared preparation and evaluation routines.
THE NEXT DECISION
What follows from the finding
Evaluate acquisition consistency and whether lighting conditions influence recognition.
Where the conclusion stops
Every source row is a recorded fault. The data cannot estimate a production defect rate or distinguish healthy plates. Batch and machine IDs are absent, so grouped exact-input holdouts do not prove factory transfer.
Related studies may reuse observations or holdouts. These are historical analyses; associations and backtests do not demonstrate commercial impact. Further model tuning needs new, untouched evaluation data.