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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 253 records · Page 14

High-Performance Polyimide Powder Coatings

Researchers at NASA's Kennedy Space Center have developed advanced powder coatings for longer-lasting, improved corrosion control. The results of preliminary tests of the coatings and their resistance to salt spray corrosion are very encouraging, and commercial partners are sought for further development.

coatings↗

Mechanisms of Interfacial Mixing in the Blown Powder Deposition of Inconel 625 to Copper Alloy for Liquid Rocket Engine Applications

There is an increasing need for functional materials to support high heat flux applications and optimize weight by using bimetallic additive manufacturing (AM). While one promising fabrication technology is the use of blown powder directed energy deposition (DED) AM, challenges remain with bi-metallic combinations. Prominent among these challenges is the need to understand mixing of materials at the bimetallic interface for forming stronger, more durable and reliable joints to extend the versatility of AM into critical applications. One application is the manufacturing of combustion chambers with copper-alloy liners and Superalloy structural jackets. In this application, material combinations are selected for effective heat dissipation, using copper, and retention of strength at high temperatures using Inconel 625. Currently, the dominant problem with this configuration is the highly variable strength of the interface and its correlation with deposition parameters. In this study, samples were fabricated at several vendors with Inconel 625 deposited onto wrought C-18150 (Cu-Cr-Zr) to form a bimetallic interface. Characterization of the interfaces were achieved through optical and electron microscopy, mechanical testing of mini tensile specimens, and electron dispersion spectroscopy to understand diffusion and bulk mixing behaviors at the joint. A theoretical framework was constructed in the context of solidification and mixing mechanisms to explain the resulting differences in the interface. This qualitative approach utilized a fluid dynamics perspective to sum the order of magnitudes of the various acting forces on the weld pool during deposition to predict convection trends based off known parameters. The predicted trends for each vendor are compared to obtain a better understanding of how mixing in the molten pool varied based on deposition parameters.

Additive Manufacturing↗

Scaleup of Microwave Powder Bed Fusion for Lunar Infrastructure Construction

NASA and its partners are seeking to establish a sustainable and permanent presence on the Moon. Like explorers on Earth, making use of natural resources upon arrival at the destination will be paramount. In-situ resource utilization of the lunar regolith will enable efficient construction of lunar infrastructure. Transforming the granular regolith into a solid can be achieved in many ways. One method is to use microwave energy to sinter the regolith into various structures. The lunar regolith has a low thermal conductivity, but microwaves can volumetrically heat it to bind the granular particles together in an energy efficient manner. One concept that is being developed and evaluated to sinter the regolith into infrastructure uses the Moon’s surface as a powder bed. Regolith would be added to the sintered lunar structure to build up layers to create structures such as landing pads, blast shields, roads, etc. Microwave systems, concept of operations, and sintering protocols are being developed to create a sub-element structure targeted for a lunar demonstration mission.

microwave↗

The Additive Manufacturing Moment Measure - A Parallel Computation Technique for Determining Build Variance in the Laser Powder Bed Fusion Process

Additive manufacturing (AM) has unique process attributes that facilitate the creation of optimized, complex, and unique parts for aerospace applications. However, the multi-scale and complicated building process for AM parts can cause unexpected build conditions that result in microstructural variability within the deposited material. The need for richer datasets and computational modeling capabilities to improve AM process reliability is a consequence of this microstructural variability. A computational approach, referred to as the Additive Manufacturing Moment Measure (AM3), addresses this need by leveraging the way AM processes fuse material in precise, incremental steps. During the layering sequence, each previous step contributes to the condition of the current step at the center of the heat source. The precise steps can be selected to systematically create a time-space point field with co-located machine input and in-situ sensor data. The sequential nature of the AM process, coupled with nearest neighbor calculations, allows for a fully parallel computation for part-scale build profile analysis. The AM3 concept introduced here enables part-scale assessment directly from build files and in-situ process monitoring sensors alike. The AM moment measures were calculated for build point fields and compared with co-located in-situ and ex-situ nondestructive evaluation and optical microscopy observations. These comparisons permit a better understanding of how the sequential process actions can affect the quality of a laser powder bed fusion (LPBF) build. Details of the AM3technique will be discussed and compared to measured LPBF part characteristics. The AM3 results indicate a strong potential to advance the qualification process for aerospace applications.

Additive Manufacturing↗

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↗