The integrative potential of LPBF-printed parts for various innovative applications depends upon the robustness and infallibility of the part quality. Eliminating or sufficiently reducing factors contributing to the formation of defects is an integral step to achieving satisfiable part quality. Significant research efforts have been conducted to understand and quantify the triggers and origins of LPBF defects by investigating the material properties and process parameters for LPBF-printed geometries using various sensing technologies and techniques. Frequently, combinations of sensing techniques are applied to deepen the understanding of the investigated phenomena. The main objectives of this review are to cover the roles of selective sensing technologies by (1) providing a summary of LPBF metal print defects and their corresponding causes, (2) informing readers of the vast number and types of technologies and methodologies available to detect defects in LPBF-printed parts, and (3) equipping readers with publications geared towards defect detection using combinations of sensing technologies. Due to the large pool of developed sensing technology in the last few years for LPBF-printed parts that may be designed for targeting a specific defect in metal alloys, the article herein focuses on sensing technology that is common and applicable to most common defects and has been utilized in characterization for an extended period with proven efficiency and applicability to LPBF metal parts defect detection.

This critical review provides heat pipe (HP) experimental data sets that contain pertinent information regarding nuclear technology that may be beneficial to researchers. Heat pipes have been shown to have a tremendously positive impact on nuclear technologies and will continue to become a more prevalent technology as more nuclear reactor concepts embrace this robust technology. Most previous reviews may focus on only a specific HP design or application, and some are backdated. This critical review extends previous efforts; integrates and summarizes previously reported HP experimental efforts; and provides updates with recently reported results in the literature for HPs in all nuclear-related applications, including space power (thermal radiators, core cooling, and electricity production), microreactors (emergency core cooling, hybrid control rods, and reactor core cooling), and HP involvement in other nuclear-related technologies (spent fuel pool cooling). The two main objectives of this critical review are (1) to facilitate the development of HP codes by outlining some of the existing experimental data sets to validate their codes and directing developers to these efforts and (2) to provide comprehensive information regarding the vast applicability of HPs used in the nuclear industry, including the theory of operation and limitations to supplement researchers in the development of new ideas for potential applications in nuclear-related technologies. The review clearly shows extensive and diverse experimental data sets for HPs developed under diverse testing conditions depending on the available nuclear application for validation purposes. Thus, this critical review is oriented to providing attention to the existing efforts rather than determining gaps in HP research.

The integration of LPBF printing technologies in various innovative applications relies on the resilience and reliability of parts and their quality. Reducing or eliminating the factors leading to defects in final parts is crucial to producing satisfactory high-quality parts. Extensive efforts have been made to understand the material properties and printing process parameters of LPBF-printed geometries that trigger defects. Studies of interest include the use of various sensing technologies, numerical modeling, and artificial intelligence (AI) to enable a better understanding of the phenomena under investigation. The primary objectives of this article are to introduce the reader to the most widely read published data on (1) the roles of numerical and analytical models in LPBF defect detection; (2) AI algorithms and models applicable to predict LPBF metal defects and causes; and (3) the integration of modeling, AI, and sensing technology, which is commonly used in material characterization and has been proven efficient and applicable to LPBF metal part defect detection over extended periods.