Characterization of IN738LC using in situ nanoindentation and crystal plasticity modeling
Bibliographic record
Abstract
This study investigates the mechanical properties of IN738LC, a precipitation-hardened Ni-based superalloy recognized for its high strength and oxidation resistance at elevated temperatures. In situ nanoindentation tests are conducted in a scanning electron microscope (SEM) to study orientation dependent mechanical response of the alloy. Electron backscatter diffraction (EBSD) is conducted on grains and around precipitates before and after tests, while high resolution imaging is conducted for slip trace analysis. The analysis is performed on both as-received and heat-treated specimens to characterize their anisotropic mechanical responses. With the use of machine learning, the critical resolved shear stresses and hardening parameters are extracted to incorporate into a crystal plasticity finite element (CPFE) model so that the calculated macroscopic response of the alloy can be compared with the measured one. In situ nanoindentation tests reveal orientation-dependent load–depth responses and misorientation patterns, which are validated against simulations that accurately capture slip traces and pile-up morphologies. EBSD measurements taken before and after nanoindentation further show the crucial role of pre-existing orientation gradients in the calculated response of the material. Additionally, TiC precipitates are identified as potential fracture initiation sites under higher stress levels.
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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 | 0.000 | 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) | 0.000 | 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".