Investigating the Impact of Deposition Pressure on CRN Coating Properties and Machining Performance With Build Up Edge Formation
Bibliographic record
Abstract
Abstract Coating properties, including hardness, elastic modulus, roughness, adhesion, and residual stress, significantly impact tool performance and wear patterns in machining. Physical vapor deposition (PVD) coatings offer tailored property combinations for specific applications, adjustable through deposition conditions like N2 gas pressure. This study focuses on customizing CrN coatings to enhance machining, particularly in reducing built-up edge (BUE) formation. Three CrN coatings with varying N2 gas pressures were deposited and characterized using X-ray diffraction (XRD) and nanoindentation testing. These techniques provided insights into the coatings’ structural and mechanical properties. The wear performance of these coatings was evaluated through a series of machining tests involving the finish turning of TiAl6V4 titanium alloy. Tool life studies were conducted to assess the coatings’ performance under machining conditions, while 3D wear volume measurements were executed to quantify the degree of tool wear and observe its progression. The results demonstrated that tailored CrN coatings with specific properties, such as a high elastic modulus, low H/E ratio and high plasticity index, were highly effective in reducing problems associated with sticking and built-up edge (BUE) formation.
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.
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) | 0.000 | 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) | 0.001 | 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 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".