Analysis of Variations in Glass Coating for Precision Forging of Titanium Components
Bibliographic record
Abstract
Abstract The extrusion of titanium billets is a complex process that requires lubrication to reduce friction and protect the dies. Typical lubricant used in extrusion is glass frit, which reduces heat transfer, friction, and protects the billet from oxidation. However, lack of knowledge on the effects of the frit’s chemical composition and spray pulverisation parameters can lead to manufacturing defects. This study employs an exploratory approach to investigate variations in the semi-automated process of coating titanium billets in an industrial context. The approach involves examining the glass frit composition, pulverisation methods and dilution strategies to determine if there are variations and their possible impact on the billet before forging. Results indicate disparities in the alumina content of the glass frit and weight differences between good and bad batches. Also, the feedstock dilution method prior to its pulverisation has been identified as a potential source of spraying variation due to the non-Newtonian behavior. Finally, the pulverisation process analysis reveals that coating thickness is affected by flow rate and viscosity variation.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| 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) | 0.001 | 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".