Experimentally validated finite element model of the rubbing and ploughing phases in scratch tests
Bibliographic record
Abstract
During single-grain grinding material is removed in three phases, namely the rubbing, ploughing and chip formation phases. The rubbing and ploughing phases are important phases to be considered as they are precursors to chip formation, where material is first removed, and represent process inefficiencies. Investigation of these phases is considerably difficult given the irregular shape of the abrasive grains and as a result these phases are often simulated by scratch tests using spherical indenters. The current work presents a finite element (FE) model of the rubbing and ploughing phases in single-grain grinding. Significant emphasis is placed on the experimental validation of the model, with a view towards its future use as an investigative tool. Single-grain grinding was simulated via a scratch test setup which produced similar surface features and the measured forces were compared against the FE predicted results with good agreement. The model developed here represents an incremental advancement of grinding FE models of the rubbing and ploughing phases by using advanced constitutive models as well as simulating the formation of a scratch.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.002 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".