Effect of Tool Design and Process Parameters on Lap Joints Made by Right Angle Friction Stir Welding (RAFSW)
Bibliographic record
Abstract
In recent decades, friction stir welding (FSW) has attracted extensive attention of academic and industrial sectors as the most considerable development in metal joining processes. FSW lap joint is an interesting alternative for rivets, fusion welds and bonding particularly in the transportation industry. In this paper, the effect of tool design and process parameters on the generated downward axial force and strength of AA6061-T6 lap joints is studied. The welds are made by a low-cost friction stir welding technique at right angle (RAFSW). The studied tool design parameters are shoulder diameter, shoulder groove depth, pin length, pin angle, pin base diameter and pin lead. Moreover, the effect of tool rotational speed, traverse speed, plunge depth and lap joint configuration is evaluated. The Taguchi method is used to design the experiments and artificial neural network (ANN) modeling is applied to predict the plunging force and the strength of the joints. The results indicate that a quality weld can be obtained at low downward axial forces during welding by proper selection of tool design and process parameters. It is identified that one can achieve a quality lap joint at traverse speeds as high as 1400 mm/min and downward axial forces as low as 3.2 kN by a low-cost RAFSW technique.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 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".