In situ correlative observation of humping-induced cracking in directed energy deposition of nickel-based superalloys
Bibliographic record
Abstract
Directed energy deposition (DED) is a promising additive manufacturing technique for repair; however, DED is prone to surface waviness (humping) in thin-walled sections, which increases residual stresses and crack susceptibility, and lowers fatigue performance. Currently, the crack formation mechanism in DED is not well understood due to a lack of operando monitoring methods with high spatiotemporal resolution. Here, we use inline coherent imaging (ICI) to optically monitor surface topology and detect cracking in situ, coupled with synchrotron X-ray imaging for observing sub-surface crack healing and growth. For the first time, ICI was aligned off-axis (24° relative to laser), enabling integration into a DED machine with no alterations to the laser delivery optics. We achieved accurate registration laterally (<10 µm) and in depth (<3 µm) between ICI measurements and the laser beam position using a single-element MEMS scanner and a custom calibration plate. ICI surface topology is verified with corresponding radiographs (correlation >0.93), directly tracking surface roughness and waviness. We intentionally seed humping into thin-wall builds of nickel super-alloy CM247LC, locally inducing cracking in surface valleys. Crack openings as small as 7 µm were observed in situ using ICI, including sub-surface signal. By quantifying both humping and cracking, we demonstrate that ICI is a viable tool for in situ crack detection.
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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".