RETRACTED: Laser Welding of UNS S33207 Hyper-Duplex Stainless Steel to 6061 Aluminum Alloy Using High Entropy Alloy as a Filler Material
Post-publication record
Source: Retraction Watch, joined by DOI. OpenAlex records retraction as is_retracted, a boolean over a state space with at least four values, so it cannot express an expression of concern, a correction or a reinstatement; it reports them as false, which reads as “fine”.
Bibliographic record
Abstract
The high entropy alloy (HEA) filler used during the fabrication method determines the reliability of HEAs for steel-aluminum dissimilar alloy configuration. HEAs have a direct impact on the formation of intermetallic compounds (IMC) formed by the interaction of iron (Fe) and aluminum (Al), and influence the size of the joint’s interaction zone. A novel welding process for Fe-Al alloy joints was developed to prevent the development of a brittle iron-aluminum interface. This research involved investigation of the possibility of using HEA powdered filler. Fe5Co20Ni20Mn35Cu20 HEAs was used as a filler for the laser joining lap configuration joining hyper-duplex stainless steel UNS S33207 to aluminum alloy 6061. This HEA has unique properties, such as high strength, good ductility, and high resistance to corrosion and wear. A tiny portion of the stainless-steel area was melted by varying the welding parameters. The high-entropy alloy (HEA) with slow kinetic diffusion and large entropy was employed to aid in producing solid solution structures, impeding the blending of iron and aluminum particles and hindering the development of Fe-Al IMCs. The weld seam was created without the use of Fe-Al IMCs,. The specimen broke at the HEAs/Al alloy interface with a tensile-shear strength of 237 MPa. The tensile-shear strength achieved was 12.86% higher than for the base metal AA 6061 and 75.57% lower than for the UNS S33207 hyper-duplex stainless steel.
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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 | 0.000 |
| 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) | 0.004 | 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".