Seam Pucker Prediction Using Neural Computing

Abstract

Seam pucker can be predicted using thickness, weight, and weft and warp (cantilever) bending stiffness as inputs to a back propagation neural network technique. Correlation coefficients between network approximation and subjective assessment of higher than 0.875 have been reported, which validate the importance of fabric properties used and establish a new prediction technique based on artificial intelligent neural computing. Argues that the integration between the instruments used and the network can provide a new industry tool for combating seam pucker.

Citation

Stylios, G. and Parsons‐Moore, R. (1993), "Seam Pucker Prediction Using Neural Computing", International Journal of Clothing Science and Technology, Vol. 5 No. 5, pp. 24-27. https://doi.org/10.1108/eb003024