Experimental Investigation on Machinability of α/β Titanium Alloys with Different Microstructures
Bibliographic record
Abstract
In the current study, Ti-6Al-4V (Ti64) and Ti-6Al-7Nb (Ti67) alloys were prepared by vacuum arc melting. The produced samples were then subjected to different heat treatment regimes. The evolved microstructures and their corresponding hardness were investigated. Computerized drilling tests using TiAlN-coated high-speed steel bits were performed to assess the machinability of the prepared specimen regarding cutting force, tool wear, and thickness of the deformed layer. It was observed that Ti64 specimens that were water quenched from either α/β or β range contained martensitic phase. In Ti67, samples showed martensite only when water quenched from the β-phase range (1070 °C). Formation of martensite resulted in higher hardness and hence led to higher cutting forces and increased tool wear during the drilling process. Machined samples with higher hardness formed a thicker subsurface deformation area (white layer) and increased burr heights. Surface roughness in Ti64 workpieces was generally higher compared to Ti67 specimens. The coat of the drilling bit was partially attacked in the as-cast specimens, which was evident by elemental N on the machining chips. The machining tool deteriorated further by increasing the workpiece hardness through martensitic formation, where elements such as Cr, V, Fe, etc. that came from the tool steel were detected.
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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".