Statistical Minimum
Statistical decision theory provides a mathematical boundary for the lowest achievable expected loss in quality control classification. The calculation of Bayes risk incorporates prior probabilities of defect occurrences and the cost weights of incorrect classification decisions. It represents the absolute limit of decision accuracy when sorting batch runs of woven cotton.
No refinement in testing protocols can reduce the error rate below this line.
Error Floor
Classification thresholds in automated textile inspection face inherent overlaps between minor weave variances and actual structural defects. When automated optical scanners sort fabrics, Bayes risk defines the unavoidable cost of misclassification that persists even with perfect algorithms. Setting this boundary helps mill managers decide when further algorithm tuning yields diminishing returns.
It represents a hard benchmark for camera performance.
Decisive Margin
Practical estimation requires mapping both false positive and false negative rates against standard fabric grading metrics. If a mill sets the defect detection sensitivity too high, the cost of rejected first-quality rolls rises, while setting it too low lets substandard goods pass to garment factories. By measuring these specific cost structures, engineers establish a formal balance that minimises total financial loss.
The resulting value guides investment in finer sensor arrays.
Quality Benchmark
Inspection contracts define acceptable defect frequencies using statistical models. Agreement negotiation relies on this mathematical limit to identify unavoidable inspection noise. It sets a baseline.
