Optical Microscopy-Aided Indentation Tests
Bibliographic record
Abstract
The contact characteristics of ceramic-metallic interactions are of critical importance in the design of high-speed ceramic rolling contact bearings. This type of interaction is not described well by traditional indentation tests since small displacements and barely discernable indentations are encountered. In this work, an optical microscopy system is described that is used to measure small indenter displacements accurately. Images of the indenter are taken throughout the test and processed using sophisticated edge detection algorithms to accurately determine the position of the center of the indenter. Thus, the indenter displacements on the order of 1μm can be measured independent of any structural flexibility present in the test apparatus. Experimental indentation tests using an alumina indenter mounted on a stainless steel post were performed and processed with the optical system. The results were compared to existing analytical models for fully elastic and elastoplastic cases as well as a finite element model developed using a Johnson–Cook plasticity material model. The comparison shows that the analytical models do not predict the experimental results well, whereas the finite element model agrees very well. Subsequent analysis of the finite element model shows that the size of the contact zone and pressure distributions, both very important in the design of bearings, can be more accurately described than the traditional analytical treatments.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".