Effects of welding parameters on mechanical properties and microstructure of TIG welded joints of AA4043
Bibliographic record
Abstract
Abstract Welding technology has played a vital role in the manufacture of complicated structures in automobile, petrochemical, aerospace, power plant, and ship industries. Tungsten Inert Gas welding process is superior technique and produces high quality weld economically. Aluminum alloy 4043 is currently preferred material in automobile, kitchen utensils and aviation industry sector due to its lightweight properties. The effective TIG welding process for AA4043 material is not studied yet with regard to micro-hardness, impact strength properties in correlation with the microstructure. Hence, consequence of welding parameters, namely welding current, voltage, and weld speed on micro-hardness, impact strength is studied based on Taguchi method. As per the developed experimental matrix, the TIG welded samples of AA4043 plates are produced using the tungsten electrode and filler wire of AA4043 in pure argon as shielding gas. Micro-hardness, impact tests are performed on welded samples and microstructures of different zones of weld are evaluated. Weld zone has highest micro hardness compared to heat affected zone (HAZ) and base metal due to presence of small grain structure. As current increases, large amount of solute enrichment in weld zone and HAZ, whereas severe grain structure change is predominantly observed in transition zone. The optimal parameters are obtained as welding current of 180 A, the voltage of 45 V and welding speed of 3.5 mm/sec for maximum micro-hardness of weld zone and impact strength of weld joint.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| 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 teacher head, 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".