Dataset and methodology on identification and correlation of secondary carbides with microstructure, wear mechanism, and tool performance for different CERMET grades during high-speed dry finish turning of AISI 304 stainless steel
Bibliographic record
Abstract
The aim of this research is to utilize reverse engineering approach for the identification of the elements and phases available in the commercial CERMET inserts with the help of characterization techniques such as Scanning Electron Microscope (SEM), Energy-dispersive X-ray spectroscopy (EDS), and X-Ray Deposition (XRD). Four commercial CERMET inserts were investigated in this research work, and the effect of the composition and phases are related to its tool wear mechanism and performance. Each CERMET insert is used to perform a turning process on a CNC lathe for machining stainless steel (SS) under the dry condition at a fixed cutting length interval. Once it completes machining for a fixed cutting length, the CERMET insert is taken out to investigate its wear mechanism with the help of SEM, EDS, XRD and using a focus-variation microscope (Alicona). A correlation analysis is performed to relate progressive tool wear mechanisms with elements and their relevant phases of various carbides. The approach of correlating wear property with the phase content will contribute to the understanding of the wear mechanism under such extreme machining conditions. It will serve as a reference for the improvement of the performance of these CERMET inserts for such harsh machining conditions by the development of protective coatings for these CERMET inserts based on the identification of the composition and phases that improves tool life and reduces wear. The data related research work can be found at "https://doi.org/10.1016/j.wear.2020.203285" [1].
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.005 |
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".