67-72Artificial neural network (ANN) and multiple
linear regression (MLR) have been used to predict the hydrophobicity of silica
sol-gel coated dyed cotton fabric using different nanoparticle concentrations, dye
concentrations, dye types and cross linker types as predictors. A total of 32
samples have been dyed with reactive and direct dyes using two dye
concentrations at HT dyeing machine. To develop nano roughness on dyed fabric,
with an aim to create super hydrophobic dyed cotton, different concentrations
of silica nanoparticles with a combination of silane hydrophobes
(alkyltrialkoxysilanes), and silane cross-linkers, i.e. tetraethoxysilane
(TEOS) and teramethoxysilane (TMOS) are applied by sol-gel technique using
dip-dry-cure process. The hydrophobicity is measured by AATCC spray rating
technique. The
coefficient of determination (R2) indicates that there is a strong
correlation between the measured and the predicted values with a trivial mean
absolute error; ANN is found to be more powerful predicting method than MLR.
The most influencing variables revealed through correlation coefficient and P-values of regression model are silica
nanoparticle and dye concentration. Empirical and statistical models have been
proposed to predict dyed cotton fabric hydrophobicity without any prior trials,
which reduces cost and time.
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