## 4 PBF-LB/M Process
### Process Optimization
#### 4.2.3 Process Monitoring and Quality Control to Improve Component Life Service
In PBF-LB/M, various in situ monitoring techniques are employed to ensure process quality, including optical, thermal, radiographic, and acoustic methods [164]: optical monitoring utilizes high-speed imaging and laser scanning to observe melt pool dynamics and surface topology, which are crucial for layer uniformity and accuracy; thermal monitoring employs infrared cameras, thermocouples, and pyrometers to track temperature distributions and detect potential overheating or melting issues; radiographic techniques, including X-ray imaging and computed tomography, offer insights into internal structures and potential defects by visualizing the internal features of the melt pool and the solidified material; and acoustic monitoring involves ultrasonic sensors and microphones to analyze sound waves, helping to identify irregularities in melt pool behavior and powder deposition. Figure 11 illustrates the classification of defects in AM, along with monitoring techniques categorized into ex situ and in situ methods. Observing and analyzing melt pool characteristics in PBF-LB/M is critical for ensuring high-quality parts, as it facilitates real-time defect detection, process optimization, and control over microstructural properties. In addition, such monitoring also contributes to improvements in surface finish and dimensional accuracy [168]. Through the application of in situ monitoring and adaptive control strategies, manufacturers can achieve parts with enhanced mechanical properties, fewer defects, and greater reliability. The following delineates the specific benefits of real-time melt pool monitoring within the PBF-LB/M process. Defects commonly associated with PBF-LB/M, including porosity, a lack of fusion, and keyholing, are closely linked to deviations in melt pool behavior. For instance, porosity may arise when the melt pool is insufficiently deep or fails to fuse the powder layers completely. By scrutinizing the depth and stability of the melt pool, it is possible to adjust laser parameters to mitigate incomplete melting. Similarly, keyholing, which occurs due to excessive energy input, can result in deep and unstable melt pools, leading to significant defects. Effective observation of melt pool characteristics facilitates the early detection and rectification of these conditions, thereby improving the overall quality of the manufactured components. In [PERSON] et al. [169], in situ X-ray imaging was employed to provide real-time visualization of the processes occurring during the PBF-LB/M of an Al-2139 alloy with TiB\({}_{2}\) additions. This advanced imaging technique enabled the observation of the rapid formation and evolution of hot cracks and porosity at the microscale, capturing critical details of crack propagation, laser-powder interactions, and pore dynamics within milliseconds. These real-time insights were pivotal in understanding the influence of various process parameters and material modifications on defect formation. Not only does monitoring the melt pool aid in the early detection of defects but it also helps identify issues related to residual stress-induced defects. Residual stresses, which can cause warping or cracking of the manufactured part, are influenced by the thermal  
Figure 11: Overview of defect types in additive manufacturing and their monitoring techniques, categorized into ex situ and in situ methods. gradients and cooling rates of the melt pool. By analyzing the melt pool temperature distribution and cooling rates, it is possible to optimize laser power, scanning speed, and path strategies to minimize thermal gradients and, consequently, residual stresses. The integration of an active contours image segmentation technique with a statistical process monitoring framework facilitates the automated detection of both in-plane and out-of-plane deviations from the nominal geometry on a layer-by-layer basis in real-time [170].