Laser cladding of Ti-Nb alloy on mild steel using fiber laser
Bibliographic record
Abstract
Low elastic modulus, superior corrosion resistant, significantly reduced field of ignition and high biocompatibility of Ti-45Nb have attracted a wide interest for various applications including medical implants, highly corrosive and oxygenated environments such as autoclaves, pressure vessels and heat exchangers. Furthermore, clad metal constructions offer a considerable cost reduction for equipment of this type in comparison to solid titanium alloy [1,2]. This paper reports the laser cladding of Ti-Nb on mild steel using pre-placed bed technique. A premixed powder of 55wt% Ti and 45wt% Nb is used and a number of tracks were successfully laid at various processing parameters. The results indicate that defect free and well bonded single clad layers with an excellent hardness (up to 1000 HV0.05) can be achieved with a relatively high dilution from the substrate. The processing parameters are set to ensure extremely short interaction times (less than ∼0.03 s), while applying a high laser power. Characterization of clad materials using microhardness, EDS, SEM and XRD has shown that applying slightly longer interaction times results in the formation of a number of brittle intermetallics between Fe and either Ti or Nb, rather than solid solutions in Ti-Nb system. The microhardness values and the types of intermetallics (formed at longer interaction times) found to be dependent on the beam interaction time.
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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".