Abstract
Abstract The use of artificial neural networks (ANNs) to develop a model of the relation between physical structure and mechanical properties of poly(ethylene terephthalate) yarns is investigated. The relation was studied on a set of 295 drawn yarn samples, produced under an exceptionally large variety of applied process conditions. The ANN, consisting of three layers of neurons, was trained using physical structure and property measurements of these 295 yarns as input and output patterns, respectively. All ten properties were fitted to the physical structure within a variance that was acceptable for quantitative use. This paper demonstrates that ANNs may be applied as a quantitative technique in solving a real-world problem. The use of ANNs resulted in an improved understanding of the complicated relation between physical yarn structure and yarn properties.
Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
