A Color Reproduction Method for Exploring the Laser-Induced Color Gamut on Stainless Steel Surfaces Based on a Genetic Algorithm

Recently, laser-induced coloring of metal surfaces has emerged as a hot topic in the field of color manufacturing. In existing research, we have not been able to find a reliable method to swiftly acquire all the color ranges achievable with current materials. This limitation hinders further research and application of laser-induced metal coloring, making it challenging to scientifically and effectively reproduce colors in images. In this study, we introduced a genetic algorithm tailored for predicting the maximization of color gamut area. By employing an elitist strategy for genetic selection and predicting the maximum color gamut among a multi-objective optimization parameter population, we successfully explored the color gamut of stainless steel. The color gamut S converged to 0.0022, offering a rapid and efficient approach for color gamut exploration. Building on this, we devised a comprehensive image color reproduction process and developed an image color gamut mapping toolkit and an image vectorization toolkit. These tools are designed for color separation, color gamut mapping, and vectorization of target images, enabling successful color reproduction through laser-induced coloring. Additionally, we conducted a color difference analysis experiment using 2 mm 304 stainless steel, demonstrating that material thickness can mitigate color cast issues in laser-induced coloring. The color difference (ΔE) values in the color reproduction experiment were 2.18, 2.97, and 2.72, respectively, indicating the reliability of image color reproduction on stainless steel surfaces. This research addresses the challenge of color gamut exploration in laser-induced coloring, presenting a novel solution for color reproduction via laser-induced coloring on metal surfaces, and holds promising applications.