## 3 Results
### Feature Importances
but at 3000 mm/s (laser power 150 W, layer thickness 0.05 mm, hatch spacing 0.035 mm, ED 42.857 J/mm\({}^{3}\)), it dropped to 90.399%, as there was not enough energy available for complete fusion of the material particles. This can be mitigated by increasing the laser power, as can be seen at 3000 mm/s with 200 W (ED 47.619 J/mm\({}^{3}\)), where the relative density improved to 91.556%. However, the results show that high energy densities can deliver excellent results at moderate speeds: At 1200 mm/s (laser power 120 W, layer thickness 0.025 mm, hatch spacing 0.035 mm, ED 114.286 J/mm\({}^{3}\)), the relative density reached 99.975%, showing that the material could tolerate high EDs with optimised parameters. For the product result at 950 mm/s (100 W laser power, 0.05 mm layer thickness, 0.035 mm hatch spacing, ED 60.150 J/mm\({}^{3}\)), the relative density dropped to 91.283%, which was probably due to process instabilities or over melting, and underlined the need for optimised parameters and further investigations. **Scanning speed \(\times\) layer thickness (9.0%):** The results showed that higher values reduced the relative density of the product. For example, at a scanning speed of 3000 mm/s and a layer thickness of 0.05 mm (product = 150), the relative density was 90.399%, while at 1500 mm/s and 0.035 mm (product = 35), it was 98.08 (all other factors remaining constant). Faster scanning with thicker layers reduced the energy density used, leading to problems with penetration and reduced particle melt quality. **Layer thickness (3.6%)** was found to be the least influential independent parameter. Thicker layers reduce the relative density slightly, as they require more energy to melt the entire material. For example, the relative density at a layer thickness of 0.025 mm (laser power 120 W, scanning speed 1200 mm/s) was 99.975%, while at 0.035 mm (same power, hatch spacing, track overlapping and speed), it was 99.263. Furthermore, the results show that the relative density decreased from 98% to 96% when the layer thickness was increased from 0.025 mm to 0.05 mm. Figure 7 shows the summarised SHAP representation, which quantifies the contributions of each input feature to the predicted part density in the model. The horizontal axis represents SHAP values that indicate the magnitude and direction of the effect of each feature on the output: positive values reflect a contribution to a higher predicted density, while negative values indicate a decrease. The features are arranged vertically in order of general importance. Each point corresponds to a single instance of data, with the colour representing the actual value of the feature--blue for low and red for high. Figure 7: SHAP plot illustrating the features’ impacts. The diagram shows that the interaction terms laser power \(\times\) scanning speed and laser power \(\times\) layer thickness had the greatest influence on the result. Both show predominantly positive SHAP values, especially at higher feature values (shown in red), indicating that an increase in these combined parameters generally leads to a higher predicted density. This likely reflects the increased energy input and improved fusion conditions achieved under such settings. The interaction term scanning speed \(\times\) layer thickness also shows a relatively strong negative contribution at high values, suggesting that a high scanning speed combined with thicker layers may reduce the energy density, resulting in a lower part density. Individual parameters such as laser power and scanning speed demonstrate moderate effects, with SHAP values closer to zero and a more balanced distribution of positive and negative contributions. Remarkably, layer thickness as a single parameter shows the smallest impact, with SHAP values close to zero. This indicates that layer thickness alone does not have a large influence on the predicted density, and its effect becomes significant mainly through interactions with other variables.