Grain disintegration and dynamic recrystallization during impact tests of additively manufactured nickel-based alloy 718
Bibliographic record
Abstract
The high-temperature dynamic mechanical response of Alloy 718 produced via laser-powder bed fusion (LPBF) was investigated through compressive Split-Hopkinson Pressure Bar (SHPB) tests. Simulating the typical service conditions of Alloy 718, the tests were conducted at temperatures ranging from 298 K to 773 K and at strain rates of 1000 s −1 to 1500 s −1 . Phenomenological material constitutive models, such as the modified versions of Johnson-Cook and Hensel-Spittel models, were developed based on the SHPB test results. Analysis of microstructural evolution under impact conditions highlighted that columnar grains with high Schmid factors tend to undergo preferential activation and dislocation pile-up. This process leads to the formation of adiabatic shear bands, grain disintegration, and intense lattice rotation, particularly at higher strain rates. Furthermore, increasing the dynamic deformation temperature facilitates the activation of discontinuous dynamic recrystallization (DRX), with strain accumulation promoting localized grain nucleation along heavily dislocated dendritic boundaries. Recognizing the limitations of phenomenological material constitutive models in accurately representing the underlying microstructural evolution, an artificial neural network (ANN)-based constitutive model employing a three-layer backpropagation learning algorithm was implemented, reducing the Average Absolute Relative Error (AARE) to 0.17%.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".