Development of in-situ laser preheating for non-weldable Ni-based superalloys manufactured by L-PBF process
Bibliographic record
Abstract
Keywords: LPBF, Cracking, Preheating, In situ measurements Abstract Non-weldable Ni-based superalloys processed by L-PBF are susceptible to solidification cracking due to the high cooling rates induced by the process. In addition, strain age cracking can occur during post-heat treatments due to γ’ precipitation. Increasing the part temperature during manufacturing is known to decrease the thermal strains induced by the L-PBF process and to promote the growth of γ’ precipitates at high temperatures, mitigating solidification and strain age cracking, respectively. This is typically done by heating the build plate, although such a strategy loses its effectiveness for large parts when the top surface is too far away from the substrate. This study aims to define an in situ preheating strategy to minimize solidification cracking by rapidly scanning the top surface during manufacturing with a five-times wider spot size. The main advantage of this approach is that the preheating is always applied at the top of the component. In addition, this approach induces homogeneous heating of the surface, reducing the cooling rate while creating a large heat affected zone (HAZ) below the surface, acting as an in situ stress-relieving heat treatment. The temperature within the part during manufacturing was measured using an innovative in situ thermal measurement system, where the part was built around a thermocouple that moved in relation to the building plate. This allowed to map the temperature within the built part for different heights and preheating conditions. This study shows that several preheating layers at regular intervals are necessary to produce a complete part without solidification cracking. SEM observations were carried out to assess the effect of preheating conditions on γ’ precipitation in different areas of the part. This allowed to observe competing factors between γ’ precipitation on the one hand, and γ’ dissolution and dislocations recovery on the other hand, depending on the thermal history.
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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 | 0.001 | 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 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".