Influence of Combined Heat Treatment and Hot Isostatic Pressure (HT-HIP) on Titanium Aluminide Processed by L-PBF
Bibliographic record
Abstract
Postprocessing is essential for improving titanium aluminide (TiAl) microstructure and part quality after using the laser powder bed fusion (L-PBF) method. It has been reported that Ti-48Al-2Cr-2Nb (%at) processed by L-PBF has internal defects and low fracture toughness. Microstructure control by heat treatment (HT) showed a significant improvement in the ductility of the material. Alternatively, hot isostatic pressing (HIPing) could be applied to reduce the residual stresses and internal defects formed during the L-PBF. Combining the benefits of these two subsequent processes into a single predetermined process is appealing for Ti-48Al-2Cr-2Nb (%at) to minimize cost. This work presents a novel strategy to postprocess L-PBF TiAl by applying combined heat treatment and hot isostatic pressing in one process, namely HT-HIP. The process includes three cycles with different conditions (i.e., temperature, time, and pressure). These conditions were determined to achieve improved part quality and microstructure. The results show that the tensile residual stresses decreased from a peak of 249 MPa in the as-built sample to compressive stresses that peaked at −90 MPa after the HT-HIP process. The number and size of internal defects could be greatly reduced. The defects were transformed into a regular spherical shape, which is good in terms of fatigue strength. Additionally, a duplex microstructure with lamellar α2/γ colonies could be introduced for better ductility. Different levels of duplex microstructure could be achieved along with the process cycles. The grain structure using EBSD analysis showed refined equiaxed grains, which demonstrate better strength after the HT-HIP process. Twinning boundaries were also observed in the HT-HIP sample. The grain orientation tendency to the build direction significantly reduced after the HT-HIP process. The nanoindentation test was applied to evaluate the nanohardness of the as-built and HT-HIP samples. It could be demonstrated that the nanohardness is dependent on the formed phases and lamellar density inside the grains. The mean hardness value was 8.19 GPa for the as-built sample, while it was 5.48 GPa for the HT-HIP sample.
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".