Automated Weave and Knit Structure Inspection
topic
Fabric structure inspection systems analyse high-resolution fabric images using texture analysis algorithms including Fourier transform frequency analysis, wavelet decomposition, and CNN feature extraction to detect weave pattern errors, missing yarns, pick or course density deviations, float defects, and stitch irregularities, comparing measured structure parameters against the specification and flagging deviations that indicate loom or knitting machine faults requiring maintenance.
Role
Enables systematic detection of fabric structural defects that arise from loom and knitting machine component wear, incorrect settings, and yarn quality variation, providing the early fault detection that prevents significant lengths of structurally defective fabric production before human inspection would identify the problem, and enabling correlation of structural defect patterns with specific machine positions or components that guides targeted maintenance intervention.