## 4 PBF-LB/M Process
### Process Optimization
#### 4.2.1 Novel Time-Save Approach for the Design of Experiments  
The PBF-LB/M process is influenced by over 50 parameters, including laser characteristics (such as the type of laser source, laser energy, scan speed, spot diameter, and focus position) and scan strategies (including hatch pattern and rotation, beam compensation, and the sequence of hatch/contour scans) [69; 130]. This large number of process parameters makes control and quality assurance challenging and time-consuming. Additionally, each parameter set must be specifically tailored to the powder and machine being used, further complicating the optimization process [131]. One approach widely used for scientific experimentation in traditional processes is the One-Variable-At-a-Time (OVAT) method, where one variable is altered while all others are kept constant to assess each variable effect independently. Once the optimal value for a variable is identified, it is set, and the next variables are examined similarly. However, this approach has significant drawbacks. As proved by [PERSON] et al., the OVAT method is unsuitable for experiments involving numerous factors and fails to effectively observe the interactions between interconnected factors [132]. Given the complexity of PBF-LB/M, analyzing OVAT is impractical due to the vast number of tests required. DoE methodologies provide a more efficient approach to optimizing process parameters, particularly when the relationships between parameters and outcomes are poorly understood. This lack of understanding can hinder the development of effective control methods. Applying DoE can quantify the correlations between parameters and process outcomes, which is crucial for improving PBF-LB/M results. Through parameter optimization, DoE can reduce surface roughness, enhance mechanical properties, and lower defect rates, ultimately increasing sustainability by extending the lifespan of components. DoE also minimizes material waste and reduces energy consumption, resulting in more efficient production at lower costs. Additionally, it fosters a deeper understanding of process interactions, encourages the use of sustainable materials, and promotes innovation in AM [133; 134]. To carry out a DoE, the following six steps should be followed [135; 136; 137]:  
* Planning: Clearly define the experiment objective and scope to ensure a specific purpose and avoid the common error of conducting experiments without a clear aim. * Screening: Identify all potential factors that could influence the outcome. This step involves selecting key variables and avoiding the mistake of \"sentimental screening\" based on personal biases rather than scientific evidence. Screening methods include full factorial, fractional factorial, and Plackett-Burman designs. * Modelling: Design the experiments by choosing relevant factors, determining their ranges, and selecting an appropriate experimental design (e.g., factorial, response surface methodology). This step includes creating a plan to vary the factors systematically. * Execution: Perform the experiments according to the designed plan, ensuring accurate and consistent data collection. This step is crucial for gathering data and model development and optimization. * Analysis and Optimization: Analyze the experimental data using statistical methods to develop a model that describes the relationship between factors and responses. Use this model to identify optimal conditions and validate the results through confirmation runs. Optimization techniques include response surface methodology designs such as Central Composite Design and Box-Behnken Design, as well as mixture designs like simplex centroid design and simplex lattice design. * Verification and Validation: Confirm the experimental findings by conducting additional tests to ensure the results are reliable and can be generalized. Analyze the results to convert data into valuable information and draw conclusions.