Recent Advancements in Machining With Abrasives
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 it