Recent Advancements in Machining With Abrasives
Bibliographic record
Abstract
Abstract This paper presents the recent advancements and forthcoming challenges for abrasive machining with specific focus on the advancement of industrial applications. The most significant advancement of abrasive machining is in grinding applications of cubic boron nitride (CBN) abrasive. The advancement of CBN wheels, application of grinding models and simulation tools, development of high stiffness multi-axis grinding machines, and high-speed spindles have contributed to the growing industrial applications of grinding with plated and vitrified CBN wheels. Sustainability of abrasive machining also received more attention during the past two decades as global Fortune 500 corporations have included sustainability as a corporate goal. Abrasive machining will continue to be a critical process for manufacturing precision components in the decades to come. The advancement and adoption of additive manufacturing creates more unique challenges for abrasive machining of complex geometrical features which were impossible a few years ago. Furthermore, strategies for abrasive machining are needed to utilize the massive amount of process data available by connected factories. Therefore, it is expected that sustainability and data analytics for abrasive machining will become a more important focus for various manufacturers.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".