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Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

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At least 1,081 records · Page 60
All sourcesNASA NTRSDOE OSTI
NASA NTRS2022

M2 Selective Laser Melting GRCop-42 Recycling Study

A powder recycling study was conducted to examine the effects of powder reuse on physical and mechanical properties of SLM GRCop-42. Twelve sequential builds were conducted, each reusing powder from the previous build topped off with virgin powder to fill the powder chamber. Powder morphology, particle size distribution, flowability, and chemistry were examined for each subsequent build, along with the mechanical properties of tensile and LCF specimens built with each powder batch. It was found that only powder flowability demonstrated a trend with increasing reuse, with flowability and cohesiveness generally decreasing over the course of the 12 builds. Minor variation in powder chemistry was observed throughout the study, however, the variations did not correlate with increasing powder reuse

Additive Manufacturing↗
NASA NTRS2023

Machine Learning to Increase the Quality and Repeatability of 3D Printing - Workflow

The imprecise nature of three-dimensional (3D) printing limits the technology’s use beyond prototyping. For production of end-use parts, such as those for aerospace applications, improvements are needed to enhance quality and repeatability. Much of the difficulty in obtaining high quality printed parts lies in finding optimum printing parameters. Currently, this requires trial and error performed by an expert. Finding the optimum printing parameters is also obfuscated by the variation in optimum parameters throughout the part due to part geometry and printer effects. To allow for locally optimized printing parameters, one can envision a machine learning algorithm that could take in an object, predict the best printing parameters, and communicate these parameters to a printer. With this scenario in mind, we developed a tool that can predict and implement locally optimized printing parameters in 3D printing. This tool consists of elements designed to detect errors in a printed part, predict the probability of local flaws occurring at each point in the part, and select the optimal local parameters for the highest quality part given hardware limitations. The results of this work were highlighted in Advanced Materials Technologies. In this paper, we will discuss in greater depth the workflow and algorithms involved with this tool that were not detailed in the journal publication.

additive manufacturing↗
NASA NTRS2022

The Space Superhighway: Systems Analysis of an In-Space Logistics Delivery Network

The number of assets in cislunar space is anticipated to dramatically increase in the coming decades. Many of these newer spacecraft are being designed to take advantage of capabilities currently in development like In-Space Servicing Assembly and Manufacturing (ISAM). Additionally, the National Aeronautics and Space Administration (NASA) intends to develop and maintain a human-lunar presence that will then serve as a steppingstone for human missions to Mars. With continued growth in space operations, the combined demand for payload and propellant delivery in cislunar space could exceed 1,000 t annually in the next decade. The Space Superhighway principally consists of a logistics network and is intended to both use and proliferate ISAM in order to meet projected payload demands. Several architecture and vehicle-level trade studies are explored in order to better understand what in-space logistics networks may be feasible. The network consists of commercial launch vehicles, resupply tankers, orbital depots, and in-space tugs. This work specifically analyzes high-level trades such as: launch and in-space vehicle propulsion systems, vehicle size, staging orbit, level of vehicle reuse, reuse method, and propellant management strategy. Each of the trades are analyzed across several destination orbits such as Low-Earth Orbit (LEO) and Near-Rectilinear Halo Orbit (NRHO). Several payload types are also considered such as storable and cryogenic chemical propellants, electric propellants, crew logistics, spares, smallsats, and satellite piece parts.

Daniel J Tiffin↗
NASA NTRSDate not supplied

Towards Qualification and Certification of Laser Powder Bed Fusion Ti-6Al-4V with In-Situ Process Monitoring and Automated Defect Detection

Qualification and certification of laser powder bed fusion (LPBF) parts are two challenges that must be answered to ensure suitability for critical applications. In-situ monitoring using high frame rate thermal and conventional optical imaging sensors is applied to the LPBF build process. Currently, the large volume of data from such sensors becomes untenable for manual inspection in production environments. This presentation serves to address this in-situ monitoring deficiency in two ways. First, a framework for managing data streams from LPBF process monitoring sensors is described. Second, two candidate image analysis techniques are presented: one is a set of heuristics that are easily interpretable, and the other is an uninterpretable convolutional neural network. These strategies are compared in terms of performance, computational expense, and speed. These methodologies represent platforms for connecting processing conditions to process modeling efforts aligned with the qualification and certification mission for LPBF Ti-6Al-4V components.

Qualification↗