Axial Force Reduction in Friction Stir Welding of AA6061-T6 at Right Angle
Bibliographic record
Abstract
Invented in 1991, friction stir welding (FSW) is a new solid state joining technique. This process has many advantages over fusion welding techniques including absence of filler material, shielding gas, fumes and intensive light, solid state joining, better microstructure, better strength and fatigue life, and etc. The difficulty with FSW is in the high forces involved especially in axial direction which requires use of robust fixturing and very stiff FSW machines. Reduction of FSW force would simplify implementation of the process on less stiff CNC machines and industrial robots. In this paper axial welding force reduction is investigated by use of tool design and welding parameters in FSW of 3.07 mm thick AA6061-T6 sheets at right angle. Attempt is made to reduce the required axial force while having acceptable ultimate tensile strength (UTS). It is found that shoulder working diameter and shoulder angle are the most important parameters in the axial force determination yet pin angle has minor effect. According to the developed artificial neural network (ANN) model, proper selection of shoulder diameter and angle can lead to approximately 40% force reduction with acceptable UTS. Regions of tool design and welding parameters are found which result in reduced axial force along with acceptable UTS.
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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) | 0.001 | 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".