Metal additive manufacturing is increasingly used as a complementary manufacturing technique in industrial settings and slowly moving from pure prototyping applications toward full production. In parallel, there is an emergence of Industry 4.0, where the applicability of concepts such as digital twins of manufacturing machines and components are being investigated. Compared to conventionally manufactured parts, typical quality metrics of metal additively manufactured components such as dimensions, roughness, porosity, and hardness are underperforming in an as-built state. As a mitigation strategy, the build chamber variables are often measured and logged by the metal additive manufacturing system to maintain a stable production environment. Thus, proper insight into the expected responses in part quality from changes in those build chamber variables is important in the pursuit of digital twins and process improvement. This sheds more light on the influence of the gas flow variables, namely gas flow speed, relative pressure, and oxygen content on the metal additive manufacturing quality metrics, specifically channel roughness, bulk porosity, average diameter, the equivalent diameter of the unobstructed cross-sectional area, and hardness of the bulk. A Design of Experiments was implemented on two laser powder bed fusion systems, namely an SLM 280 processing 316 L stainless steel and an SLM 500 processing Ti6Al4V. The current work found that surface oxidation of 316 L and Ti6Al4V components may be classified based on simple red, green, and blue (RGB) color constituent analysis. The influence of gas flow variables was found to be different in the two investigated SLM systems, suggesting a high dependency on the processed material. Oxygen content in the build chamber had the highest standalone effect on the selected quality metrics, while the gas flow speed had the lowest standalone effect. The second-order effects were found to be, in general, more significant than the main effects. The findings of the current work is a step towards an improved understanding of the interaction effects of gas flow conditions on typical quality metrics of metal additive manufactured components. By the creation of simple but computationally fast response surface models, in-line assessments may be carried out and the effect of process variability on component quality may be evaluated in-situ while being one step away from the full feedback control implementation in the digital twin. Following the methodology of the current work for other laser powder bed fusion systems enables the generation of 3D point cloud visualizations for decision making under uncertainty.