Role of Ti3AlC2 MAX phase on characteristics of in-situ synthesized TiAl intermetallics. Part IV: mechanical properties
Bibliographic record
Abstract
In this study, the 4th part of a series of publications on the sintering and characterization of TiAl-Ti3AlC2 composite materials, the mechanical properties were measured and discussed. For this purpose, different contents of synthesized Ti3AlC2 reinforcement (10, 15, 20, 25, and 30 wt%) were added to metallic Ti and Al powders, then ball-milled and manufactured by spark plasma sintering (SPS) for 420 s at 900 ºC under 40 MPa. Flexural strength, fracture toughness and Vickers hardness were measured by 3-point technique, SENB method, and indentation technique, respectively. Increasing the Ti3AlC2 content resulted in improvement of the mechanical properties, so that TiAl-25 wt% Ti3AlC2 composite showed the best flexural strength and Vickers hardness (270 MPa and 4.11 GPa, respectively). Increasing amount of Ti3AlC additive had no significant effect on fracture toughness. Densification improvement, in-situ formation of Ti2AlC, and limitation of grain growth were recognized as the reasons of mechanical properties enhancement. In contrast, further addition of Ti3AlC2 (30 wt%) decreased the mechanical properties due to the reduction of density and formation of more Ti2AlC agglomerates in grain boundaries.
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 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".