Multi-objective optimization and experimental investigation of friction stir welding under Minimum quantity lubrication
Bibliographic record
Abstract
Aluminium is widely used in the aerospace, marine, and transportation industries. However, achieving defect-free, high-quality welds using conventional welding processes is challenging. Friction stir welding (FSW) is a promising solid-state welding process that is environmentally friendly, produces high-quality welds, and improves the mechanical and other properties of aluminium and other lightweight materials. This study examines the welding force, power consumption, and surface roughness of friction stir-welded 6061-T651 and 5052-H32 aluminium alloys under similar and dissimilar circumstances using the minimum quantity lubrication (MQL) process. The main variables of the MQL system, including flow rate, nozzle orientation, and nozzle diameter, were analysed using analysis of variance. A multi-objective model was used to predict the optimal levels, and grey relational analysis techniques were applied for optimization. Furthermore, this study provides a clear mechanism for using MQL during FSW. The results indicate that a flow rate of 7.5 ml/h reduces welding forces by 25% and 4% compared to 5 ml/h and 10 ml/h, respectively. Additionally, the 7.5 ml/h flow rate reduces power consumption by 20% and 10% compared to 5 ml/h and 10 ml/h, respectively. In addition, it improves the surface quality. The orientation nozzle angle of 60° yielded slightly better results than those at the other levels. Similarly, for the nozzle diameter, both 2.5 mm and 3.75 mm showed slightly better results in terms of welding forces, power consumption, and surface roughness.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 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 | 0.001 |
| 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".