Advanced Parameter Optimization for Laser Engraving Machines via Genetic Algorithms

Laser engraving may be used in a variety of industries, from medicine to defense, and it has many uses that require high-quality precision production. However, in practice, operators have to adjust the laser settings manually, which can result in wasted material and poor color quality and even decrease productivity. Current optimization approaches mostly concentrate on single objectives, making it impossible to co-optimize engraving quality and production efficiency simultaneously. In this paper, an approach based on a multi-objective genetic algorithm, a combination of NSGA-II, SPEA2, and MOEA/D, is proposed to automatically establish the relationship between CMYK color attributes, which are extracted from images of engravings, and laser parameters (power, speed, and frequency). Anodized aluminum 6061 was laser-processed using an SPI 30W fiber laser. While the proposed framework is general, the experimental validation in this study was specifically constrained to this material. The results also indicate that MOEA/D converges in a short time and becomes relatively stable after 20 generations. NSGA-II results in solutions that are more diverse, and SPEA2 offers a good trade-off between the speed of convergence and solution size. This approach resulted in optimization in terms of both a decrease in material used and color matching between manual operations, with the average CMYK improvement being up to 28%. Our results indicate that multi-objective evolutionary optimization is feasible for the optimization of efficiency and quality in laser cutting.