## 4 PBF-LB/M Process
### Process Optimization
#### 4.2.1 Novel Time-Save Approach for the Design of Experiments
A review of the literature reveals useful applications of DoE in optimizing the processes of additively manufactured aluminum alloys [138; 139; 140; 141]. [PERSON] et al. [138; 139] conducted two studies focusing on optimizing processing parameters and evaluating heat treatment effects on the surface quality and relative density of PBFed AlSi10 Mg alloy. They employed a three-factor, three-level full factorial DoE for both studies. In the first study, they investigated the influence of processing parameters and various heat treatment conditions on relative density, achieving maximum densification exceeding 99% under optimal conditions of 320 W laser power, 900 mm/s scanning speed, and a 25% overlap rate. In the second study, they focused on optimizing processing parameters to achieve the best surface quality. Their findings, supported by analysis of variance (ANOVA) and regression analysis, revealed that higher laser power increased surface roughness while scanning speed initially decreased roughness but showed a slight increase at higher speeds. [PERSON] et al. [140] highlight the application of the Taguchi experiment design method to optimize the PBF-LB/M process for the AlSi10 Mg alloy. By employing ANOVA, the most significant factors influencing the process were identified, with laser power being the most critical parameter affecting relative density. A regression analysis provided a linear regression equation for the model, enabling the prediction of optimal processing parameters. [PERSON] et al. [141] investigate the impact of PBF-LB/M process parameters on the quality of AlSi10 Mg and Al6061 aluminum alloys using a full factorial DoE. Key findings include that optimal energy densities for AlSi10 Mg (50-60 J/mm\({}^{3}\)) and Al6061 (102.8 J/mm\({}^{3}\)) significantly influence relative density, porosity, surface roughness, and dimensional accuracy. AlSi10 Mg achieved a maximum relative density of 99.7%, while Al6061 reached 98.7%. Surface roughness decreased with increasing energy density, and dimensional accuracy varied accordingly. Despite being an efficient method to quickly define optimal process parameters for PBF-LB/M production, the DoE approach still requires producing and analyzing a significant number of bulk samples. This process demands a substantial amount of time, as well as high levels of powder and energy consumption. Several alternative techniques have emerged in recent years to accelerate parameter optimization while minimizing powder usage, time, and costs, among which the SST approach stands out. This approach is widely adopted in research and involves scanning a laser track on a single powder layer spread onto a substrate. This approach is based on the idea that PBFed parts are made of overlapping SSTs, therefore, the part properties strongly depend on the geometry of each SST and the interaction among them. Unlike massive sample production, SST scanning requires only a minimal amount of powder and a few fractions of a second as scanning time [142; 143; 144; 145]. In the SST approach, only laser power and scanning speed are varied while the layer thickness is kept constant. This approach allows for extensive exploration of power-speed combinations due to the rapid production and analysis capabilities. By evaluating the width and continuity of the SSTs from a top view, as well as their cross sectional morphologies, a narrower range of power-speed combinations suitable for bulk production can be swiftly identified. To evaluate the SST quality, the widely used method is the cross-section analysis. In [PERSON] et al. [142] the geometrical characteristics of the SST cross-section, including the width, growth, depth, and contact angles, were examined to investigate the material melting and consolidation behavior, thus determining its suitability for PBF-LB/M production. However, the SST cross-sectional analysis involves a complex and time-consuming sample preparation procedure, and operator error could strongly affect the manual on-top evaluation. To overcome these issues, [PERSON] et al.