Statistics-based Quality Control of Patterns

This study addresses a statistics-based testing procedure aimed at optimizing quality assurance for custom-made and made-to-measure (CM/M2M) clothing patterns.The procedure focuses on identifying machine-detectable issues in patterns before they progress to the expensive and time-intensive manual tailoring phase, thereby streamlining the testing cycle and reducing both time and costs.By analysing patterns for individuals with similar body measurements and applying statistical methods, the study identified potential design errors in pattern pieces based on measurable properties such as perimeters, areas, and contour-defining lines.Advanced statistical techniques, including residual analysis, Cook's distance, and Mahalanobis distance, were employed to detect outliers and pinpoint potential construction errors.Further analysis of line properties using predictive models-such as linear regression, random forests, generalized additive models (GAM), and rpart decision trees-revealed that a high frequency of outliers often correlates with construction anomalies.This research demonstrates that predictive modelling and outlier detection are effective tools for identifying errors in CM/M2M pattern construction, contributing to improved garment accuracy and production efficiency.