Turbidimetry for the stability evaluation of emulsions used in machining industry
Bibliographic record
Abstract
Emulsified fluids are used in many industrial and consumer areas, for instance as products in the food or health industry as well as technical fluids in the machining industry. Metalworking fluids (MWF) are used as coolants and lubricants in metalworking processes. During their usage MWF emulsions may change their physical and chemical properties, which influences their performance and decrease the physical stability and therefore their lifetime. This article discusses results of turbidimetric spectra measurement of MWF emulsions to be used for process control, MWF quality monitoring and formulation purposes. Therefore, laboratory experiments have been carried out investigating the physical stability. Metal working emulsions have been treated and destabilised by different concentrations of salts. The destabilisation process was monitored by undiluted turbidity measurements and evaluated by the temporal change of the wavelength exponent. Thus, it was possible to determine specific conditions, for example a specific critical salt concentration for maintaining, stability of the MWF formulations.
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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 | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".