Investigating the high-impact deformation behavior of induced-nano precipitation hardened in718 alloy
Bibliographic record
Abstract
Superalloys are the preferred materials for modern high-rotational complex components due to their exceptional ability to maintain critical properties such as strength, oxidation/corrosion resistance, even under extreme temperatures and dynamic impacts. This study investigates the dynamic impact response of nanoprecipitation-hardened 718 alloy (NPH-718 alloy) under various loading conditions. The wrought nickel-based NPH-718 alloy was transformed into a nanostructured state through a precise heat treatment process involving controlled cooling rates (28℃∙s‒1 to 30℃∙s‒1). A mechanical compression test, leading to failure, was employed to assess the alloy's ability to withstand dynamic impacts. The compressive dynamic behavior of the alloy at high strain rates (4000 s‒1 to 7500 s‒1) and temperatures ranging from ‒180℃ to 750℃ was evaluated using a custom-built direct impact Hopkinson pressure bar apparatus. The flow data obtained for NPH-718 alloy exhibited sensitivity to thermally activated processes. Consequently, as the temperature increased at a constant high loading rate, both flow stress and adiabatic effect increased. Conversely, at a constant deformation temperature, the flow characteristics exhibited an increase as the loading rate decreased. This study establishes key trends in the flow stress, adiabatic effect, temperature, and the strain rate sensitivities of NPH-718 alloy, offering valuable insights for design and performance evaluation purposes. It underscores the significant influence of temperature and strain rate on the flow behavior of NPH-718 alloy, further solidifying its reliability in demanding applications.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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) | 0.001 | 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 source (direct Gemma or distilled Codex), 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".