Seam Puckering Objective Evaluation Method for Sewing Process
This addresses quality control issues in textile manufacturing by reducing human error, but it is incremental as it applies existing image processing techniques to a specific domain.
The paper tackles the problem of subjective seam puckering evaluation in sewing by developing an automated method using Fourier transform and Kohonen Map neural networks to classify puckering defects into five quality grades.
The paper presents an automated method for the assessment and classification of puckering defects detected during the preproduction control stage of the sewing machine or product inspection. In this respect, we have presented the possible causes and remedies of the wrinkle nonconformities. Subjective factors related to the control environment and operators during the seams evaluation can be reduced using an automated system whose operation is based on image processing. Our implementation involves spectral image analysis using Fourier transform and an unsupervised neural network, the Kohonen Map, employed to classify material specimens, the input images, into five discrete degrees of quality, from grade 5 (best) to grade 1 (the worst).