Research and Progress in Laser Welding of Wrought Aluminum Alloys. II. Metallurgical Microstructures, Defects, and Mechanical Properties
Bibliographic record
Abstract
With the wide application of aluminum alloys in automotive, aerospace, and other industries, laser welding has become a critical joining technique for aluminum alloys. In this review, the research and progress in laser welding of wrought aluminum alloys are critically discussed from different perspectives. The primary objective of the review is to understand the influence of welding processes on joint quality and to build up the science base of laser welding for the reliable production of aluminum alloy joints. Two main types of industrial lasers, carbon dioxide (CO2), and neodymium-doped yttrium aluminum garnet (Nd:YAG), are currently applied but special attention is paid to Nd:YAG laser welding of 5000 and 6000 series alloys in the keyhole (deep penetration) mode. In the preceding article of this review (part I), the laser welding processing parameters, including the laser-, process-, and material-related variables and their effects on welding quality, have been examined. In this part of the review, the metallurgical microstructures and main defects encountered in laser welding of aluminum alloys such as porosity, cracking, oxide inclusions, and loss of alloying elements are discussed from the point of view of mechanism of their formation, main influencing factors, and remedy measures. The main mechanical properties such as hardness, tensile and fatigue strength, and formability are also evaluated.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".