An On-Line 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 adaptation of the sewing param eters to improve stitch formation and seam quality can occur. Needle penetration forces and presser foot forces are captured and d...

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Bibliographic Details
Published in:Textile research journal 1996-08, Vol.66 (8), p.521-528
Main Authors: Barrett, G.R., Clapp, T.G., Titus, K.J.
Format: Article
Language:English
Subjects:
Classification
Fabrics
Laminates
Neural networks
On-line systems
Plies
Sewing
Wavelet transforms
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Summary:A sewing system is described that classifies both the fabric type and number of plies encountered during apparel assembly, so that on-line adaptation of the sewing param eters to improve stitch formation and seam quality can occur. Needle penetration forces and presser foot forces are captured and decomposed using the wavelet transform. Salient features extracted using the wavelet transform of the needle penetration forces 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 for 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 control sewing parameters in real time.
ISSN:0040-5175
1746-7748
DOI:10.1177/004051759606600806