Feasibility of multilayer solid-state deposition via lateral friction surfacing for metal additive manufacturing
Bibliographic record
Abstract
Lateral friction surfacing is a novel solid-state deposition process in which the radial surface of the rotating consumable tool is forced into the substrate surface, facilitating material transfer. This technique is an excellent alternative to create thin and ultra-smooth metallic deposit layers for repairing damaged surfaces or improving corrosion and wear resistance. The lateral friction surfacing approach results in a deposition process with lower generated process temperatures than conventional friction surfacing, which leads to reducing thermal effects on the microstructures and mechanical properties of the deposits. In this study, the extent of material transfer to the substrate was explored via multiple passes of the tool in an effort to create multiple layers of deposited material. Two types of substrate plates with different surface roughness as well as two different strategies for employing the consumable tools were experimented. A comprehensive assessment through conducting real-time force measurement, surface roughness measurement, hardness testing, optical microscopy, infrared thermography, scanning electron microscopy, and EDS analysis was performed to characterize the process and the fabricated deposits. The thickness of the coating was found to vary through work material transfer to the substrate and reverse material transfer from the coating to the radial surface of the rod, resulting in an approximately steady-state deposit thickness. The reverse material transferring process from the coating to the radial surface of the rod through rubbing off the previously fabricated coatings limits plasticizing more consumable material and built-up material.
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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) | 0.001 | 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".