Predicting torque of worsted singles yarn using an efficient radial basis function network-based method
Article
Article Title | Predicting torque of worsted singles yarn using an efficient radial basis function network-based method |
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ERA Journal ID | 36381 |
Article Category | Article |
Authors | Tran, Canh-Dung (Author) and Phillips, David G. (Author) |
Journal Title | Journal of the Textile Institute |
Journal Citation | 98 (5), pp. 387-396 |
Number of Pages | 10 |
Year | 2007 |
Place of Publication | United Kingdom |
ISSN | 0039-8357 |
0040-5000 | |
0368-4482 | |
1754-2340 | |
Digital Object Identifier (DOI) | https://doi.org/10.1080/00405000701475650 |
Web Address (URL) | https://www.tandfonline.com/doi/full/10.1080/00405000701475650 |
Abstract | The torque in single-spun yarns is an inherent property of the twisting and bending of staple fibres during the formation of yarn combined with the effect of applied tension on the yarn. The consequences of yarn torque are well known and are widely observed as yarn instability, e.g., yarn rotation under tension; local snarling and entanglement at low loads, and as distortion in fabric, i.e., edge-curl and skewing in knitted fabric. In this paper, a method for predicting the yarn torque based on the radial basis function networks is presented and evaluated. This method uses a 'universal approximator' based on neural network methodology to minimize noise during training of the network and to approximate the yarn torque as a function of the geometrical and physical parameters of yarns (twist, linear density) and the applied load. The current method is an integral radial basis function network-based approach suitable for textile engineering and gives very good prediction of yarn torque across a range of yarn structural parameters and test conditions. |
Keywords | radial basis function networks; feed forward neural networks; yarn structural parameters; yarn instability; intrinsic torque |
ANZSRC Field of Research 2020 | 490302. Numerical analysis |
401413. Textile technology | |
401706. Numerical modelling and mechanical characterisation | |
Public Notes | File reproduced in accordance with the copyright policy of the publisher/author. |
Byline Affiliations | Commonwealth Scientific and Industrial Research Organisation (CSIRO), Australia |
Institution of Origin | University of Southern Queensland |
https://research.usq.edu.au/item/q0794/predicting-torque-of-worsted-singles-yarn-using-an-efficient-radial-basis-function-network-based-method
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