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In recent years, technological advancements have led to the industrialization of the laser powder bed fusion process. Despite all of the advancements, quality assurance, reliability, and lack of repeatability of the laser powder bed fusion process still hinder risk-averse industries from adopting it wholeheartedly. The process-induced defects or drifts can have a detrimental effect on the quality of the final part, which could lead to catastrophic failure of the finished part. It led to the development of in situ monitoring systems to effectively monitor the process signatures during printing. Nevertheless, post-processing of the in situ data and defect detection in an automated fashion are major challenges. Nowadays, many studies have been focused on incorporating machine learning approaches to solve this problem and develop a feedback control loop system to monitor the process in real-time. In our study, we review the types of process defects that can be monitored via process signatures captured by in situ sensing devices and recent advancements in the field of data analytics for easy and automated defect detection. We also discuss the working principles of the most common in situ sensing sensors to have a better understanding of the process. Commercially available in situ monitoring devices on laser powder bed fusion systems are also reviewed. This review is inspired by the work of Grasso and Colosimo, which presented an overall review of powder bed fusion technology.

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A new chiral benzimidazole‐pyrrolidine has been devised, which exhibits excellent activities in aminocatalyzed aldol reactions, leading to aldol products in high yields and enantioselectivities in the presence of an equimolar amount of a Brönsted acid. This organocatalyst has demonstrated remarkable reactivities in aldol processes even with equimolar amounts of aldehyde and ketone in THF. A discussion of the role of the Brönsted acid as a co‐catalyst is provided along with some applications of this new class of organocatalyst in Robinson annelation and α‐amination processes. (© Wiley‐VCH Verlag GmbH & Co. KGaA, 69451 Weinheim, Germany, 2007)

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Micrographic image analysis, tomography and the Archimedes method are commonly used to analyze the porosity of Selective Laser Melting (SLM)-produced parts and then to estimate the relative density. This article deals with the limitation of the relative density results to conclude on the quality of a part manufactured by additive manufacturing and focuses on the interpretation of the relative density result. To achieve this aim, two experimental methods are used: the image analysis method, which provides local information on the distribution of porosity, and the Archimedes method, which provides access to global information. To investigate this, two different grades of aluminum alloy, AlSi7Mg0.6 and AM205, were used in this study. The study concludes that an analysis of the metallographic images to calculate the relative density of the part depends on the areas chosen for the analysis. In addition, the results show that the Archimedes method has limitations, particularly related to the choice of reference materials for calculating relative density. It can be observed, for example, that, depending on the experimental conditions, the calculation can lead to relative densities higher than 100%, which is inconsistent. This article shows that it is essential that a result of relative density obtained from Archimedes measurements be supplemented by an indication of the reference density used.