Seam Pucker Prediction Using Neural Computing

G. Stylios, R. Parsons-Moore

Research output: Contribution to journalReview articlepeer-review

21 Citations (Scopus)


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.
Original languageEnglish
Pages (from-to)24-27
Number of pages4
JournalInternational Journal of Clothing Science and Technology
Issue number5
Publication statusPublished - 1 May 1993

ASJC Scopus subject areas

  • Business, Management and Accounting (miscellaneous)
  • Materials Science (miscellaneous)
  • General Business,Management and Accounting
  • Polymers and Plastics


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