Discrimination of D2 steel roughness after hard milling
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
This article discusses the discrimination of machined surfaces following the milling of 62 HRC hardened AISI D2 steel, a material used in the industrial sector due to its considerable harnesses and resistance to wear. The main aim of this research is to determine the most appropriate surface roughness parameter, be it Ra evaluated on two orientations (Ra (0) and Ra (90)) or Sa (arithmetic surface roughness), in order to effectively distinguish between surfaces that appear visually similar. To this end, a series of milling experiments were carried out by varying several machining parameters, including cutting speed, feed rate and tool trajectory angle. Following machining, the surfaces produced were analyzed using precise roughness measurements. The experimental data were then studied using a machine-learning classification technique, with the aim of assessing the ability of each roughness parameter to differentiate the various surfaces created. The most decisive indicator of clustering is the Ra (0) parameter, as shown by the results.
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 | 0.000 |
| 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