AN ONLINE FABRIC CLASSIFICATION TECHNIQUE USING A WAVELET-BASED NEURAL-NETWORK APPROACH

A sewing system is described that classifies both the fabric type and number of plies encountered during apparel assembly, so that on-line a daptation of the sewing parameters to improve stitch formation and sea m quality can occur. Needle penetration forces and presser foot forces are captured and decomposed using the wavelet transform. Salient feat ures extracted using the wavelet transform of the needle penetration f orces form the input to an artificial neural network, which classifies the fabric type and number of plies being sewn, A functionally linked wavelet neural network is trained on a moderate number of stitches fo r five fabrics, and can correctly classify both fabric type and number of plies being sewn with 97.6% accuracy. This network is intended for use with dedicated DSP hardware to classify fabrics on-line and contr ol sewing parameters in real time.