Abstract
This paper presents a new quantitative approach to the study of Asian lacquers using surface metrology, and two data science approaches: feature engineering and convolutional deep neural networks, as used in machine vision or image recognition applications. The types of Asian lacquers and additives have a quantifiable impact on the topography of the resulting surface. To understand the unaged and aged characteristics, 15 different formulas of Asian lacquer were prepared using laccol, thitsiol and urushiol with the most common additives: oils, pigments and resins. These were studied with the surface metrology instrumental technique of confocal microscopy.
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