Abstract
Engineering yarn quality has remained a challenge for researchers and shop-floor technicians for a long time. The advent of high-speed computers has helped researchers in facing this challenge in a better manner. Attempts have been made to apply linear programming, mechanistic models and statistical models to the assessment of engineering yarn quality. Various studies have shown that an artificial neural network (ANN) can engineer yarn more accurately than those methods. In this chapter, the engineering of ring yarn and air-jet yarn is discussed. Prediction of fibre properties and process parameters from the required yarn quality can be made with acceptable accuracy using ANN.
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.
