Pre–Placed Metal Laser–Additive Manufacturing on Grade 5 Titanium Substrate: Layer Formation, Oxidation Resistance, and Wear Resistance
Bibliographic record
Abstract
The laser parameters and additive materials were investigated for the laser–additive manufacturing of titanium alloys. A pre–placed metal–strip–based method for 3D printing titanium components was proposed. Before laser processing, the metal strips were fixed to a Grade 5 titanium substrate using resistance spot welding. The effect of the processing conditions (laser power: 1.5–3.0 kW; scanning speed: 3–9 mm·s−1; one or three layers; 1, 6, or 12 passes; Grade 2 and Grade 5 as additives) on the microstructures, oxidation resistance, and wear resistance of the as–printed samples was investigated. The results showed that the microstructure consisted of α′ martensite, oxygen–stabilized α, transformed β that contains coarse and fine acicular α, titanium oxides, and carbides. Cracks were observed, particularly near the upper surface of the three–layer samples. The cracks were suggested to have formed due to cumulative residual stresses and the formation of oxygen–stabilized α and α′ martensite that might cause embrittlement. Both oxidation and wear tests were conducted to verify the improved performance. After 55 h of oxidation, the as–printed samples showed mass gains of 0.029–0.035 g·cm−2, which were smaller than those of the substrate (0.039 g·cm−2).
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".