Reinforcement Learning to Improve Color Adjustments in the Ceramic Industry

The ceramic industry is a highly competitive millenary sector with a substantial economic impact in several countries translated into a high global volume business, especially in exports. Reliability in the color development process is critical, ensuring that the produced pieces achieve the defined requirements. Nevertheless, current strategies for color formulation or adjustment are quite manual and subjective, essentially based on a trial-error process. These conventional procedures lead to the development of unnecessary pieces until the target color is achieved, which translates into a waste of raw materials and working time. In this paper, we present an automated approach based on Reinforcement Learning (RL) to improve color adjustments in the ceramic industry. By using the spectral data of the available components (pigments and glazes), the proposed algorithm provides the best formula to achieve the target color, i.e. the list of components for the mixture and corresponding quantities. Two datasets were used: the NTU Watercolor Pigments Spectral Measurement dataset; and the Matceramica Ceramics Spectral Measurement dataset. Three RL algorithms were trained and compared for benchmarking purposes: Deep Q-learning. (DQN), Advantage Actor-Critic (A2C) and Continual RL Without Conflict (OWL). The A2C and OWL models obtained similar performances for the NTU dataset with a mean Delta E-ab* of 0.668 and 0.733, respectively. For the Matceramica dataset, OWL yielded better results with a mean Delta E-ab* of 3.258. These results demonstrate the potential of the proposed approach to be integrated into an AI-powered software solution that optimizes the iterative process of color (re)creation in ceramic glazes.