engleski

Comparison of Kubelka - Munk Theory and Fuzzy Logic in Colour Matching

One of the parameters of greatest interest in today's right on time production of colored surfaces is colour quality assurance. Also, preciseness in colour matching is found to be one of the most serious practical problems, which should be considered from both ecological and economical points of view. The most common approach to computer colour estimation and colour reproduction of the substrates is achieved through Kubelka– Munk theory which is being widely used in colour industry. Although, this theory has enough accuracy for most industrial applications, it was found to be inexact in many circumstances. The systematic discrepancy between the model and the reality is a serious indicator how theory is either too much simplified or built on wrong assumptions. In this work, application of Kubelka – Munk theory versus fuzzy logic in colour matching was examined in order of exposing main differences between these two approaches and illustrating possibilities of eventual fuzzy logic use in colour match prediction operations. The experiments were performed on cotton knitted fabrics of two different surface characteristics. Textile samples were dyed using Kubelka – Munk recipes, with yellow direct dyestuff in five corresponding concentrations (0, 25% ; 0, 75% ; 1, 4% ; 2% ; 3%). After analyses of dyed samples, the recipe correction of concentration was performed using Kubelka – Munk and fuzzy logic colour calculations, and those two approaches were compared through the colour difference values. Results obtained showed differences considering standard sample smaller using fuzzy logic corrected concentrations than those obtained through Kubelka Munk. Results were obtained calculating dE values.

Color match prediction; correction; fuzzy logic; Kubelka– Munk.

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