Improving Color Mixture Predictions in Ceramics using Data-centric Deep Learning

Ceramics is a millenary industrial sector with relevant financial impact for several countries. Efficiency in color mixing is crucial in the ceramic industry, both in terms of staff time and consumables costs. Traditional color mixing methods usually consist of manual processes based on in-depth domain knowledge of basic color theory or color models like Kubelka-Munk. Thus, the efficiency of these procedures is highly dependent on the technician's expertise, being challenging for novices to acquire these skills and be proficient. This work explores the usage of Deep Learning to generate color mixture predictions in ceramic glazes. The proposed solution is based on spectral data of ceramic components (pigments and glazes) and, based on their respective quantities, simulates the color mixing result in a wholly digital way. Given the lack of freely available datasets, we started by exploring our approach in the NTU Watercolor Pigments Spectral Measurement dataset. We then translated the collected knowledge to the Matceramica Ceramics Spectral Measurement dataset, which was specifically created in the ambit of this work. By using data-centric optimization techniques to improve our model interactively, the best performance was achieved by fully connected neural network model, with mean Delta E-ab* of 1.57 and 2.35 for the NTU and Matceramica datasets, respectively. These results demonstrate the potential of the proposed approach to be integrated into an AI-powered software solution that improves color mixing procedures in the ceramic industry.