Industrial research on evolution and prediction of hardwood color

Abstract

Color prediction in dyed wood is a difficult task since it involves the analysis of light propagation through a complex media where scattering and absorption processes are present. Kubelka-Munk based models are usually proposed to make those predictions. Here, an oak wood color prediction tool is presented with the Kubelka-Munk theory and self-learning procedures as the basis of the model. Color prediction lies on the joint contribution of both the dying material and the wood substrate, each characterized by their previously obtained colorimetric and spectral properties. An identification of wood and dyes through the study of their optical properties is shown from which the necessary parameters are obtained for the different applications. The model allows to predict with good accuracy the resulting color in wood through the L*C*hº coordinates when mixing either water or solvent based dyes in different proportions for dying a wood substrate. Furthermore, the influence of applying dyes mixture by either hand with a brush or machine with a roller coating, and also that of varnishing are studied.