Additive manufacturing (AM) of ceramics presents both exciting opportunities and significant challenges, particularly with the laser‐based AM processes. Ceramics are known for their special properties, such as high strength, corrosion resistance, and temperature stability, but their inherent brittleness and high processing demands make AM more complex. This review provides an updated overview of the most common AM techniques for ceramics, including direct energy deposition, binder jetting, laminated object manufacturing, and material extrusion‐based techniques. However, the focus is placed on the laser powder bed fusion (LPBF) of ceramics, a technique that has gained increasing attention for its ability to fabricate complex ceramic parts with enhanced quality. The review delves into the key causes of critical defects commonly observed in LPBF, such as porosity, cracking, spattering, and surface roughness. Recent advancements in addressing these issues are discussed, along with the limitations of current defect prevention strategies. Furthermore, the review provides an updated analysis of the mechanical properties of LPBF‐fabricated ceramics, giving insights into how processing parameters influence the performance of ceramic LPBF‐printed parts. Modeling and simulation techniques are also reviewed, highlighting their role in enhancing understanding of ceramic behavior during LPBF. Overall, this review highlights recent progress and current challenges in ceramic AM techniques, while exploring future research opportunities, such as process optimization and defect prevention strategies.

Innovations in technology assist the manufacturing processes in producing high-quality products and, hence, become a greater challenge for quality engineers. Control charts are frequently used to examine production operations and maintain product quality. The traditional charting structures rely on a response variable and do not incorporate any auxiliary data. To resolve this issue, one popular approach is to design charts based on a linear regression model, usually when the response variable shows a symmetric pattern (i.e., normality). The present work intends to propose new generalized linear model (GLM)-based homogeneously weighted moving average (HWMA) and double homogeneously weighted moving average (DHWMA) charting schemes to monitor count processes employing the deviance residuals (DRs) and standardized residuals (SRs) of the Poisson regression model. The symmetric limits of HWMA and DHWMA structures are derived, as SR and DR statistics showed a symmetric pattern. The performance of proposed and established methods (i.e., EWMA charts) is assessed by using run-length characteristics. The results revealed that SR-based schemes have relatively better performance as compared to DR-based schemes. In particular, the proposed SR-DHWMA chart outperforms the other two, namely SR-EWMA and SR-HWMA charts, in detecting shifts. To illustrate the practical features of the study’s proposal, a real application connected to the additive manufacturing process is offered.