## 3 Results and Discussion  
### Gamut Exploration  
In the color domain exploration genetic algorithm labeling experiment, 100 mm*100 mm 304 stainless steel with a thickness of 1 mm was used. The main experiments were divided into five processes: initialization, labeling, optimal selection, recombination, and termination, corresponding to the initialization of the labeling parameter population, laser-induced coloring, selection of the optimal laser labeling parameters to predict the maximization of color domain area, recombination of next-generation labeling parameters for genetic variation, and termination of genetic labeling by achieving convergence. For the initialization population, this study chose to use a matrix test to cover the parameter population to ensure the coverage of the initial parameter population, which included four parameters: scanning speed, laser frequency, laser power, and scanning interval. In the experiment, the laser parameters were divided into 20 groups, with equal distributions as the initial population to ensure that the initial parameter population could cover the entire effective range of parameters, ensuring the effectiveness and efficiency of the experiment. The corresponding distributions are shown in Table 2. The dual scanning method was used to label the sample and perform color measurement on the labeled sample using the LAB mode of the X-Rite Ci64. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Number**} & \multirow{2}{*}{**Power (W)**} & **Scanning Speed** & **Repetition Frequency** & **Scanning Interval** & **Pulse Duration** \\  & & **(mm/s)** & **(kHz)** & **(mm)** & **(ns)** \\ \hline
1 & 3.00 & 50 & 20 & 0.001 & 4 \\
2 & 3.26 & 152 & 124 & 0.0015 & 4 \\
3 & 3.52 & 254 & 228 & 0.002 & 4 \\ \(\vdots\) & \(\vdots\) & \(\vdots\) & \(\vdots\) & \(\vdots\) & \(\vdots\) \\
18 & 7.42 & 1784 & 1788 & 0.0095 & 4 \\
19 & 7.68 & 1886 & 1892 & 0.01 & 4 \\
20 & 8.00 & 2000 & 2000 & 0.01 & 4 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Initial population laser parameter distribution. Figure 3: Example showcasing the color gamut mapping process in color reproduction. **(a)** The original image. **(b)** Four color separation images derived from the original image. **(c)** Four color separation images after color gamut mapping. **(d)** Image mapped to the color gamut. Figure 4a-f shows representative six-generation experimental results of this experiment. In this paper, we adopted the color gamut maximization prediction to explore the whole color gamut; converted the color parameters from CIELAB color space to CIE 1931 color coordinates; used the position information of the color parameters in color coordinates to predict the maximum color gamut of the current generation of populations, as shown in Figure 4g-l; and used Equation (7) for the calculation of selecting the current optimal color gamut's representative color. We added the algorithm for selecting the optimal color gamut's representative color by hue to the genetic algorithm, and there were four groups of optimal color gamut representative colors for each generation of marking, which represented the maximum range of the color gamut that could be achieved by the current marking samples, as shown in Figure 4m. These four color parameter combinations were retained in the next generation of the population and participated in the genetic process of mutation and crossover, which ensured that the optimal genes were continuously updated and inherited. The current theoretical color gamut was calculated by means of gamut area maximization prediction, and each subsequent generation was integrated with all previous marking samples to repeat the gamut area maximization prediction operation.