Additive manufacturing (AM) techniques are increasingly applied with precision and are recognized as valuable tools across various stages of product development and production. The demand for innovative and customized products tailored to individual users is growing rapidly, and AM enables the fast creation of such items, facilitating timely market launches. Among the existing AM technologies, powder bed fusion (PBF) has gained considerable attention due to its ability to produce high-quality parts automatically. This is attributed to its compatibility with a wide range of materials and the superior quality of the final components. Despite its advantages, PBF faces challenges that must be addressed to establish it as a reliable manufacturing method. Various issues, such as poor dimensional accuracy, variations in mechanical properties, defects, residual stresses, surface irregularities, etc., limit its application in high-value, mission-critical products. The primary factors affecting the quality of parts produced by PBF are the processing parameters. Because process parameters are directly related to microstructure development and process-induced defects, optimizing parameter settings and preventing defects such as melt pool geometry is key to ensuring the production of high-quality AM components. Hence, in this work, the role of process parameters and other factors affecting the part quality is discussed in detail. Moreover, the manuscript discusses the role of advanced tools, such as machine learning, in situ monitoring, simulations, etc., to enhance the part quality during PBF-based AM.
Tiwari et al. (Mon,) studied this question.