Parameter Optimization for Dissimilar Aluminum Alloys Joined Using Friction Stir Additive Manufacturing: A Screening Study
Bibliographic record
Abstract
ABSTRACT Due to the requirement for better strength‐to‐weight ratios, the utilisation of aluminum alloys is rapidly expanding. Lightweight components are of utmost importance in most industries, particularly in transportation, aviation, maritime, automotive, and other industries. These lightweight engineering materials are hard to be joined utilising traditional fusion joining techniques, necessitating the development of alternative joining techniques. The novel friction‐stir additive manufacturing (FSAM) technique, based on the concept of friction stir welding (FSW), can be used to combine aluminum alloys additively in their solid state. This work examines the effects of several process parameters (tool rotational speed, tool tilt angle, and tool transverse speed) on tensile strength and hardness using a 3‐factor L9 Taguchi designed experiment. Three cases were explored, one were AL 6061 was welded to AL 7075, one where AL 7075 was welded to AL 6061, and one where the data were mixed to get an “average” effect representative of large additively manufactured parts. A detailed ANOVA (including both main effects and interactions analyses) provided clear guidance on the optimization of the parameters for several objectives. This work will contribute to the development and wider use of FSAM in both industrial and academic research settings by providing a useful dataset and clear parameter selection guidance. The results of this research indicate that the FSAM methodology could be utilized to fabricate large defect‐free structures, which can be a suitable replacement for the traditional Al6061 material used in automotive and aerospace sectors.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 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".