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New Modelling and Process Optimisation Approach for the False-Twist Texturing of Polyester

Research and development

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Abstract:

After the wave of ISO 9000 certification, a large number of enterprises started to accumulate a great amount of data regarding their processes. False-twist texturing plants used these data to set up a process and improve their operations. This article shows that data mining, partial least squares modelling and genetic algorithm optimisation can provide further use for these data to benefit the company in many areas, such as setting up adequate process parameters without requiring an expert to do so, providing the customer with the requirements that will fulfill his needs, simplifying machine changes, and reducing lot changes. The results show that the model and optimisation structure put together can find multiple solutions for machine parameters by providing the multiple product properties or quality levels desired. The prediction of yarn properties, such as linear density (Dtex), elongation, tenacity and boiled water shrinkage were made with R2 between 0.80 and 0.99.

Tags: polyester filament, texturing, false twist, multivariate statistics, optimisation, partial least squares, genetic algorithm.±

Citation: Silva E. A., Paiva A. P., Balestrassi P. P., Silva C. E. S.; New Modelling and Process Optimisation Approach for the False-Twist Texturing of Polyester. FIBRES & TEXTILES in Eastern Europe 2009, Vol. 17, No. 6 (77) pp. 57-62.

Published in issue no 6 (77) / 2009, pages 57–62.

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