On thermal expansion behavior of invar alloy fabricated by modulated laser powder bed fusion
Bibliographic record
Abstract
In the present research, structural analyses are conducted on Invar (64% Fe-36% Ni) samples manufactured by modulated laser powder bed fusion (LPBF) under different laser power regimes. Within the selected process window, the results obtained from X-ray computed tomography analysis indicate that the relative density of as-built samples increases with an increase in laser power. In addition, it is observed that irregular-shaped pores with the longest axis normal to the building direction are significantly reduced with increasing the laser power. Microstructural investigation suggests that with increasing the laser power, a transformation from conduction to transition and keyhole modes occurs. In X-ray diffraction patterns of the as-built samples, only fcc γ-phase is detected and no reflection of bcc α-phase is found. Texture measurements show that the intensity of 〈110〉 crystallographic orientation along the building direction of the as-built samples increases with increasing laser power. Coefficient of thermal expansion of the as-built samples is very low and comparable to that of conventionally manufactured Invar alloy. Furthermore, as-built samples fabricated at lowest (250 W) and highest (400 W) laser powers exhibit the lowest and highest thermal expansion displacement, respectively. Finally, the thermal expansion behavior and its correlation with structural integrity and texture are discussed.
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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".