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Title: Automation in colouration technology to predict dyeing parameters for desired shade and fastness
Authors: Chakraborty, Ananya
Kaur, Pankaj Deep
Chakraborty, J N
Keywords: Artificial neural network;Cloud computing;Cotton;Full factorial design;Sulphur dye
Issue Date: Dec-2019
Publisher: NISCAIR-CSIR, India
Abstract: In this study, dyeing parameters, such as dye conc., sodium sulphide conc., salt conc., and time, have been statistically framed through full-factorial design software to generate sets of experimental variables. Cotton has been dyed using all these sets of variables separately, and then evaluated for respective surface colour strength (K/S), and colour fastness properties, such as fastness to light, washing and rubbing. The outputs thus generated are then analyzed using ANN to generate a big data, by which dyer can predict any shade. This will help in eliminating the rigorous laboratory trials and forecasting colour strength & quality of dyeing well before the dyeing process is materialized. The whole data sets are then uploaded in cloud computing to enable to acquire the data. It is observed that by assigning diffent values of K/S on cloud, the dyeing parameters can be obtained to achieve desired output in further application.
Page(s): 450-458
ISSN: 0975-1025 (Online); 0971-0426 (Print)
Appears in Collections:IJFTR Vol.44(4) [December 2019]

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