ND:YAG laser welding of AA6061: Experimental differences between the TEE and LAP joint configurations
Bibliographic record
Abstract
Effect of laser welding parameters such as power, out-of-focus length, welding speed and feeding speed on weld properties are studied in tee and lap configurations. Two millimetre thick plates of AA6061 were welded in Tee joint configuration using a cw/Nd:YAG laser and AA5356 wire as the filler metal. The same set-up and wire feeding were used for the lap joining of two millimetre thick square tubing and plates of AA6061. Combination of Taguchi and E.M. design of experiments was carried out to explore efficiently the multidimensional volume of welding parameters, to optimise these parameters and to compare the experimental differences between the two joint configurations. Samples were characterised by optical microscopy, SEM and hardness measurements. The weld properties of interest were weld fillet size, penetration depth, concavity size and heat affected zone dimensions measured by the hardness profiles. The process parameters and their respective and interactive effects on the final responses have been investigated. The results indicate the interlateral relationship between laser process parameters and responses and fundamental differences between the Tee and lap joint laser welding. Difference of hardness profiles between these two configurations highlighted the difference of cooling flows of the two set-up.
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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) | 0.001 | 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 | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".