Automation in colouration technology to predict dyeing parameters for desired shade and fastness
In this study, dyeing parameters, such as dye conc., sodium sulphide conc., salt conc., and time, have been statisticallyframed through full-factorial design software to generate sets of experimental variables. Cotton has been dyed using all thesesets 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 bigdata, by which dyer can predict any shade. This will help in eliminating the rigorous laboratory trials and forecasting colourstrength & quality of dyeing well before the dyeing process is materialized. The whole data sets are then uploaded in cloudcomputing to enable to acquire the data. It is observed that by assigning diffent values of K/S on cloud, the dyeingparameters can be obtained to achieve desired output in further application
Artificial neural network;Cloud computing;Cotton;Full factorial design;Sulphur dye
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