Application of Artificial Intelligence in Modeling a Textile Finishing Process

Textile products with faded effect are increasingly popular nowadays. Ozonation is a promising finishing process treatment for obtaining such effect in the textile industry. The interdependent effect of the factors in this process on the products’ quality is not clearly known and barely studied. To address this issue, the attempt of modeling this textile finishing process by the application of several artificial intelligent techniques is conducted. The complex factors and effects of color fading ozonation on dyed textile are investigated in this study through process modeling the inputs of pH, temperature, water pick-up, time (of process) and original color (of textile) with the outputs of color performance (\(K/S, L^*, a^*, b^*\) values) of treated samples. Artificial Intelligence techniques included ELM, SVR and RF were used respectively. The results revealed that RF and SVR perform better than ELM in stably predicting a certain single output. Although both RF and SVR showed their potential applicability, SVR is more recommended in this study due to its balancer predicting performance and less training time cost.

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