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Goldsby, Desarae

Publications and source records attributed to Goldsby, Desarae.

Advanced manufacturing of 3D custom boron-carbide collimators designed for complex environments for neutron scattering

Scattered-beam collimation is a very useful method to reduce unwanted backgrounds and to boost the desired sample signal instead. This approach is of particular interest for samples contained within a complex environment that gives rise to much unwanted parasitic scatter. As neutron scattering instrument and techniques advances, small samples are becoming of more and more interest, which necessitates optimized collimation. Here, in this work, we describe a concept for the design and fabrication of advanced scattered-beam collimation 3D printed from B 4 C specifically tailored for samples contained within a complex environment. This concept is demonstrated through the use of a diamond anvil cell for high pressure experimentation, a technique that very typically requires small samples. The collimators here are designed through a modeling procedure via Monte Carlo neutron ray tracing that encompasses the entire system: the instrument, the complex environment and the collimator. Since the first approach of simply scaling up of the print-size was not successful, a novel concept of a multi-part alternate-blade collimator was developed. This approach addresses printing constraints but gives greater flexibility in design. Its performance is computationally compared against an unprintable progressively tighter blade collimator to assess the effect of alternating blades. No strong difference was observed. Its performance was validated through experimentation at the Spallation Neutron Source. The results emphasize the critical importance of ultra-high precision alignment while showing good overall agreement between simulation and experiment and underscore the feasibility of the method and its real-world application.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗

Exploration of Binder Jet Additive Manufacturing for Automotive Heat Sink Component Fabrication

ORNL (Contractor) and Magna Services of America Inc. (Participant) collaborated to determine the feasibility of binder jet additive manufacturing (BJAM) for the fabrication of copper components for automotive heat sink applications. This Phase 1 collaboration focused on printing copper heat sinks that rely on capillary effect to move fluids rather than mechanical pumps in electric vehicles to extend the battery range and life. Partial sintering of copper powders deposited via BJAM was hypothesized to aid capillary effect to improve the heat transfer. Further, BJAM offers the potential for scalability at a production level.

36 MATERIALS SCIENCE↗

Additively Manufacturing Nitinol Shape Memory Alloys for Advanced Actuator Designs

The objective of this research was to understand the role of feedstock production in the phase transformation behavior of additively manufactured Ni-Ti alloys for advanced actuator design. Industrial adoption of additively manufactured Ni-Ti alloys depends on the ability to produce repeatable phase transformation behavior, quantified here by the austenite to martensite transformation on heating. Small variations in the alloy composition may have a significant effect on the temperature at which this transformation occurs. This project showed that the powder characteristics play an important role in determining this behavior. Increases in the surface area per unit volume of the powder, either as a function of size distribution or morphology, have the effect of reducing the Ti content in the alloy through the formation of Ti-rich oxides on the powder surface, which has the effect of depressing the transformation temperature. Preferential Ni vaporization during additive manufacturing can partially offset this effect. To achieve repeatable results, it is important to understand the effect of powder oxidation, and to control the powder characteristics.

36 MATERIALS SCIENCE↗

Layer-wise Imaging Dataset from Powder Bed Additive Manufacturing Processes for Machine Learning Applications (Peregrine v2021-03)

This dataset contains layer-wise powder bed images from three different powder bed printing technologies – laser powder bed fusion, electron beam powder bed fusion, and binder jetting. This dataset was collected and annotated using the internally-developed Peregrine software tool and is designed primarily to facilitate research into anomaly defect detection using image segmentation or similar techniques. A total of 20 layers are provided for each printing technology, with each layer of data consisting of one or more calibrated images and an annotation file containing pixel-wise ground truth labels. The ground truths were labeled by domain experts, typically printer technicians. Data in this release were collected at Oak Ridge National Laboratory between 2016 and 2020 and were compiled in March 2021.

36 MATERIALS SCIENCE↗

Methods for rapid identification of anomalous layers in laser powder bed fusion

In situ process monitoring is a key requirement for increased industry acceptance of powder bed Additive Manufacturing. As sensing technologies increase in maturity, attention must also be given to effective data exploration techniques. These data are often high-resolution and multi-modal, with each build consisting of thousands of layers. Here, in this paper, the authors propose two methods enabling users to rapidly identify layers of interest within a build. Both methods leverage results from deep learning based segmentations of in situ powder bed images. The first method is an unsupervised “reverse layer search” algorithm while the second method uses supervised machine learning.

36 MATERIALS SCIENCE↗