Lateral friction surfacing: experimental and metallurgical analysis of different aluminum alloy depositions
Bibliographic record
Abstract
Lateral friction surfacing, a solid-state deposition process, is a novel friction surfacing technique. In this approach, frictional heat and plastic deformation result in deposition of consumable material from the radial surface of a tool onto a substrate. This paper presents a comprehensive assessment of lateral friction surfacing of AA2011, AA6061, and AA7075 aluminum alloys, with particular focus on the impacts of process parameters on the coating properties. The influence of process variables such as tool rotational speeds, normal applied forces, and type of consumable materials was investigated on the process temperature, physical, and metallurgical characteristics of the deposits using optical microscopy, infrared thermography, scanning electron microscopy, and EDS. This study exhibits that the lateral friction surfacing approach enables the deposition of ultra-thin and smooth layers of different aluminum alloys. Furthermore, the temperature generated in this technique was low enough to avoid plasticizing the substrate and intermixing between the consumable material and substrate, which mitigates the thermal impacts on the grain structures and metallurgical characteristics. The lateral friction surfacing performance of the different alloys can be partially explained by their material properties. High input energy provided by high normal forces and tool rotational speeds may result in failure in the deposition process of materials with lower thermal conductivity and melting point, which emphasizes on limitations for the process parameters during the process.
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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 | 0.001 | 0.001 |
| 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".