Optimisation of tribology of Alfa Laval separator screw gear systems
Bibliographic record
Abstract
Gears are machine elements essential for mechanical transmission. Wear of gear teeth can causechanges in their profile geometry, causing vibration, noise, and subsequent gear failure. In this thesisproject, a twin disc tribometer has been used to mimic the actual Alfa Laval separator screw gearsystems. An investigation of the wear of different material pairs has been done using a twin disctribometer machine. In the twin disc tribometer, the material surface for one disc was bronze and thematerial for the counter disc surface was varied with steel of different types. Each bronze-steel pair wastested against different loading, lubricating and surface roughness conditions. For the wear tests, twodifferent loads, 75 N and 150 N and two different types of lubricants, Polyalphaolefin (PAO) andPolyalkylene Glycol (PAG) were taken. Wear tests were also done with the Applied Nano Surfaces(ANS) triboconditioning (TCG) steel disc samples which are surface treated to obtain a lower surfaceroughness. To obtain the wear volume and wear rate values the disc pairs were further analysed usinglaboratory equipment and computer software such as the digital microscope, optical interferometer andMountainsLab premium 9. Analytical calculations for minimum film thickness and thermally correctedminimum film thickness between the disc surfaces were obtained using the Dowson-Hamrock equationand Gupta formula, respectively. It has been found that the wear on the bronze disc is minimum if thecounter steel discs are Applied Nano Surfaces (ANS) of TCG 1 type followed by counter disc as casehardened steel, all of it at 75 N and with Polyalphaolefin (PAO) lubricant oil.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".