Computational image marking on metals via laser induced heating

Laser irradiation induces colors on some industrially important materials, such as stainless steel and titanium. It is however challenging to find marking configurations that create colorful, high-resolution images. The brute-force solution to the gamut exploration problem does not scale with the high-dimensional design space of laser marking. Moreover, there exists no color reproduction workflow capable of reproducing color images with laser marking. Here, we propose a measurement-based, data-driven performance space exploration of the color laser marking process. We formulate this exploration as a search for the Pareto optimal solutions to a multi-objective optimization and solve it using an evolutionary algorithm. The explored set of diverse colors is then utilized to mark high-quality, full-color images.