AFRL Additive Manufacturing Modeling Series: Challenge 4, In Situ Mechanical Test of an IN625 Sample with Concurrent High-Energy Diffraction Microscopy Characterization

Menasche, David B.; Musinski, William D.; Obstalecki, Mark; Shah, Megna; Donegan, Sean P.; Kenesei, Péter; Park, Jun-Sang; Shade, Paul A.

doi:10.1007/s40192-021-00218-3

Cited by 15 publications

(4 citation statements)

References 30 publications

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“…2 . Due to limited real EBSD data, our goal is to train this model on a large and diverse synthetic dataset and demonstrate that it can generalize to real EBSD data, which we evaluate on two nickel superalloy EBSD volumes, one for alloy IN625 36 , 37 and one for alloy IN718 38 , 39 . The following repository contains the code for our method: https://github.com/hdong920/ebsd_slice_recovery .…”

Section: Methodsmentioning

confidence: 99%

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A lightweight transformer for faster and robust EBSD data collection

Dong,

Donegan,

Shah

et al. 2023

Sci Rep

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Three dimensional electron back-scattered diffraction (EBSD) microscopy is a critical tool in many applications in materials science, yet its data quality can fluctuate greatly during the arduous collection process, particularly via serial-sectioning. Fortunately, 3D EBSD data is inherently sequential, opening up the opportunity to use transformers, state-of-the-art deep learning architectures that have made breakthroughs in a plethora of domains, for data processing and recovery. To be more robust to errors and accelerate this 3D EBSD data collection, we introduce a two step method that recovers missing slices in an 3D EBSD volume, using an efficient transformer model and a projection algorithm to process the transformer’s outputs. Overcoming the computational and practical hurdles of deep learning with scarce high dimensional data, we train this model using only synthetic 3D EBSD data with self-supervision and obtain superior recovery accuracy on real 3D EBSD data, compared to existing methods.

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Section: Methodsmentioning

confidence: 99%

“…Now, we seek to understand how well our model transfers to real data by running our trained model on IN625 36 , 37 and IN718 38 , 39 . For both volumes, we subdivide each volume into nonoverlapping subvolumes such that all slices are of height and width 64 voxels.…”

Section: Methodsmentioning

confidence: 99%

A lightweight transformer for faster and robust EBSD data collection

Dong,

Donegan,

Shah

et al. 2023

Sci Rep

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“…The investigation was performed within the context of the Air Force Research Laboratory (AFRL) AM Modeling Challenge Series. Specifically, Challenge Problem 4 in the Series solicited participants to make blind predictions of grain-averaged strain tensors in 28 challenge grains at six specific stress states given a 3D microstructural image of a tensile specimen and its corresponding macroscale engineering stress–strain response. , The tensile specimen was produced using LPBF with IN625 powder. The microstructure data and grain-averaged elastic strains provided by AFRL were experimentally characterized with high-energy X-ray diffraction microscopy (HEDM) during an in situ tensile test performed at the Advanced Photon Source 1-ID-E beamline at Argonne National Laboratory. − Given the time constraints of the challenge, Spear’s team neglected residual strains in the EVPFFT model used for challenge submission.…”

Section: Mechanical Response Modelingmentioning

confidence: 99%

“…Tensile specimen with magnified view of experimentally characterized microstructure of IN625 manufactured by laser powder bed fusion for the AFRL AM Modeling Challenge. , At right is the predicted axial strain field from micromechanical modeling …”

Section: Mechanical Response Modelingmentioning

confidence: 99%

Multiphysics Modeling Framework to Predict Process-Microstructure-Property Relationship in Fusion-Based Metal Additive Manufacturing

Tan,

Spear

2024

Acc. Mater. Res.

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Metrics & MoreArticle Recommendations CONSPECTUS: Additive Manufacturing (AM) technology produces three-dimensional components in a layer-by-layer fashion and offers numerous advantages over conventional manufacturing processes. Driven by the growing needs of diverse industrial sectors, this technology has seen significant advances on both scientific and engineering fronts. Fusion-based processes are the mainstream techniques for AM of metallic materials. As the metals go through melting and solidification during the printing processes, the final microstructure and hence the properties of the printed components are highly sensitive to the printing conditions and can be very different from those of the feedstock. It is critical to understand the process-microstructure-property relationship for the accelerated optimization of the processing conditions and certification of the printed components. While experimentation has been used widely to acquire a mechanistic understanding of this subject matter, numerical modeling has become increasingly helpful in achieving the same purpose.In this Account, the authors review their ongoing collaborative effort to establish a multiphysics modeling framework to predict the process-microstructure-property relationship in fusion-based metal AM processes. The framework includes three individual modules to simulate the dominating physics that dictate the process dynamics and microstructure evolution during printing as well as the responses of the printed microstructure to specific mechanical loadings. The process model uses the material properties and processing conditions as the inputs and simulates the laser-material interaction, multiphase thermo-fluid flow, and fluid-driven powder motion. It has successfully revealed the physical causes of depression zone shape variation as well as powder motion during the laser powder bed fusion process. The microstructure model uses the thermal history of the printing process and the material chemistry as the inputs and predicts the nucleation and growth of multiple grains in the multipass and multilayer printing processes. It has been used to understand the effects of inoculation and thermal conditions on grain texture evolution. The property models use microstructure data from simulations, experimental measurements, or statistical analyses as the inputs and leverage various computational tools to predict the mechanical response of the AM materials. These models have been used to quantitatively evaluate the effects of grain structure, residual strain, and pore and void defects on their properties and performance. While this and many other modeling works have significantly grown our collective knowledge of the process-microstructure-property relationship in fusion-based metal AM processes, efforts should be further invested in developing advanced theories and algorithms for the governing physics, leveraging data-driven approaches, accelerating simulation speed, and calibrating/validating models with controlled experimental measurements, among ...

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AFRL Additive Manufacturing Modeling Series: Challenge 4, 3D Reconstruction of an IN625 High-Energy Diffraction Microscopy Sample Using Multi-modal Serial Sectioning

Chapman

Shah

Donegan

et al. 2021

Integr Mater Manuf Innov

Self Cite

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High-energy diffraction microscopy (HEDM) in-situ mechanical testing experiments offer unique insight into the evolving deformation state within polycrystalline materials. These experiments rely on a sophisticated analysis of the diffraction data to instantiate a 3D reconstruction of grains and other microstructural features associated with the test volume. For microstructures of engineering alloys that are highly twinned and contain numerous features around the estimated spatial resolution of HEDM reconstructions, the accuracy of the reconstructed microstructure is not known. In this study, we address this uncertainty by characterizing the same HEDM sample volume using destructive serial sectioning (SS) that has higher spatial resolution. The SS experiment was performed on an Inconel 625 alloy sample that had undergone HEDM in-situ mechanical testing to a small amount of plastic strain (~ 0.7%), which was part of the Air Force Research Laboratory Additive Manufacturing (AM) Modeling Series. A custom-built automated multi-modal SS system was used to characterize the entire test volume, with a spatial resolution of approximately 1 µm. Epi-illumination optical microscopy images, backscattered electron images, and electron backscattered diffraction maps were collected on every section. All three data modes were utilized and custom data fusion protocols were developed for 3D reconstruction of the test volume. The grain data were homogenized and downsampled to 2 µm as input for Challenge 4 of the AM Modeling Series, which is available at the Materials Data Facility repository.

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AFRL Additive Manufacturing Modeling Series: Challenge 4, In Situ Mechanical Test of an IN625 Sample with Concurrent High-Energy Diffraction Microscopy Characterization

Cited by 15 publications

References 30 publications

A lightweight transformer for faster and robust EBSD data collection

A lightweight transformer for faster and robust EBSD data collection

Multiphysics Modeling Framework to Predict Process-Microstructure-Property Relationship in Fusion-Based Metal Additive Manufacturing

AFRL Additive Manufacturing Modeling Series: Challenge 4, 3D Reconstruction of an IN625 High-Energy Diffraction Microscopy Sample Using Multi-modal Serial Sectioning

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