Microstructural Characterization and Mechanical Properties of Fiber Laser Welded CP-Ti and Ti-6Al-4V Similar and Dissimilar Joints
Bibliographic record
Abstract
In this research, the microstructures and mechanical properties of similar and dissimilar autogenous joints of 3 mm thick commercially pure titanium (CP-Ti) and Ti-6Al-4V welded by ytterbium fiber laser (Yb:YAG) were investigated. Two sets of laser power and welding speed were selected in such a way that the heat input remained constant. Microstructural characterization of the joints was investigated by an optical microscope, and mechanical properties were determined by hardness and tensile tests. The only defects found were porosity and underfill, and no signs of lack of penetration and solidification cracks were observed in any of the joints. Microstructural evaluation of the fusion zone (FZ) showed that in similar Ti-6Al-4V joint, a supersaturated nonequilibrium α′ martensite was formed due to rapid cooling associated with laser welding. In similar CP-Ti, coarse equiaxed grains were observed in the FZ. Unlike the similar joints, a clear interface was observed between the heat-affected zone (HAZ) and the FZ in both the CP-Ti and Ti-6Al-4V sides in dissimilar joints. Among all the joints with different weld parameters, similar Ti-6Al-4V showed the highest strength and the lowest ductility. In similar CP-Ti and dissimilar joints, fractures took place in the CP-Ti base metal, but all the Ti-6Al-4V similar joints failed in the FZ. Significant changes in the strength and hardness with varying laser power and welding speed implied that the mechanical properties of the weld fusion zones were not entirely governed by the heat input but were also affected by individual welding parameters.
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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 | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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".