Effect of Tool Geometry and Process Parameters on Strength of Various Friction Stir Spot Welded Lap Joints
Bibliographic record
Abstract
Friction stir welding (FSW) is widely used in aerospace and automotive industries but this process can also be use to join various metals, which are for daily use.As aluminium alloys are used to manufacture most of the kitchen utensils but rivets are most commonly used to join them.For such type of joints, friction stir spot welding (FSSW) can be a good replacement or alternative to rivets.In the present study, FSSW lap joint is been carried out in a bench-drilling machine (spindle speed range of 750 RPM -1341 RPM) to investigate the feasibility of such type of joints in small machine.The aim of this study is to investigate the effects of tool geometry and spindle speed on strength of friction stir welding at different joint.Two different types of tools i.e. circular probe tool and square probe tool are taken into consideration.The variation of welding strength with numbers of spot weld in the joint is also been discussed.The welding process is carried out for two types of joints, i.e., single spot FSW and double spot FSW lap joints.The welding material chosen for the process is aluminium alloy.The temperature recorded at the thermomechanical effected zone signifies that the welding is faster by square probe tool, which takes 20 to 25 seconds to get weld and reaches the highest temperature.The improvement of the process can make FSSW easier to use as a substitute of rivets.
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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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".