Free-form laser consolidation for producing metallurgically sound and functional components
Bibliographic record
Abstract
Functional metallic components can be built layer by layer from a computer-aided design model, by using an optical fiber coupled Nd:YAG laser beam along with the simultaneous delivery of desired metal/alloy powders through a nozzle into the molten pool. Building of shapes using various alloys, including 316 L stainless steel, Ni-base IN-625 superalloy, and M4 tool steel, have been investigated. The components built using the free-form laser consolidation are metallurgically sound, free of cracks and porosity. Surface finish on the order of 1–2 μm (Ra) can be obtained on the consolidated samples. The microstructure of the laser-consolidated samples is similar to the rapidly solidified materials. Under certain conditions, directionally solidified microstructure can also be obtained. X-ray diffraction analysis reveals that laser-consolidated material maintains the same phase structure as the original powder. The tensile properties of the laser-consolidated IN-625 alloy and 316 L stainless steel are comparable to the respective wrought materials. The microhardness of the consolidated M4 tool steel reaches to approximately Hv 1000. The polarization testing results indicate that laser-consolidated IN-625 alloy and 316 L stainless steel show a similar corrosion resistance as the respective wrought material in 3.5% NaCl solution.
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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 | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".