Tool Wear Characteristics and Effect on Microstructure in Ti-6Al-4V Friction Stir Welded Joints
Bibliographic record
Abstract
In the present paper, tool wear and the rate of wear during friction stir welding (FSW) of Ti-6Al-4V alloy are investigated. A conical tungsten carbide tool was used to produce butt-type friction stir welded joints in two-millimeter thick Ti-6Al-4V sheets. An original design of a movable pin allowed for the examination of the tool damage for each process condition. The influence of tool degradation on the quality of the welded joints and the damage brought to the microstructure are examined and discussed. For this purpose, optical and scanning electron microscopies as well as EDX analyses were used to examine the tool wear and the resulting macrostructures and microstructures. The type and nature of the defects are also analyzed as a function of FSW processing parameters. Important geometry and weight variations were observed on the pin and shoulder for all welding conditions, in particular when low tool rotation and travel speeds were used. Experimental results also show that the radial wear of the pin is not uniform, indicating the presence of important frictional temperature gradients through the thickness of the joint. The maximum wear was measured at a location of about one millimeter from the pin root center. Finally, tool rotation was determined as the most significant process parameter influencing both tool wear and microstructure of the joints.
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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 | 0.001 |
| 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) | 0.000 | 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 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".