Structure-Property Evaluation of CrN Coatings Developed for BUE Dominated High-Speed Machining Applications
Bibliographic record
Abstract
Various nitrides, such as chromium nitride and titanium nitride, find extensive use in cutting tools, micromechanical devices, and medical implants due to their exceptional physical, mechanical, and chemical properties. These coatings exhibit superior hardness compared to high-speed steel and cemented carbide along with notable protective capabilities against corrosion and wear. These coatings have been successfully used to enhance the properties of cemented carbide and steel tools while safeguarding their surfaces. By adjusting deposition parameters like N2 gas pressure, the properties of PVD coatings can be tailored to effectively withstand specific dominant wear modes during machining. The study investigates and demonstrates that CrN coatings can be specifically engineered to have distinct mechanical and tribological properties by adjusting the N2 gas pressure, which enhances machining performance in cases where BUE formation occurs. A comprehensive coating characterization was conducted for each CrN coating studied. Wear performance assessments of the various CrN-coated WC tools were carried out during dry finish turning of SS 304. Additionally, high temperature coating characterization was performed for the best-performing in house deposited coating (nitrogen gas pressure of 4 Pa, bias voltage of -50 V) and a commercial coating, up to 450°C. The results highlighted the influence of N2 gas pressure on the structural, mechanical, and tribological properties of CrN coatings. The findings indicate that coatings with a comparatively low H/E ratio (while maintaining higher elastic modulus values), low roughness, moderate residual stress, high plasticity index, and high toughness exhibited superior performance when machining sticky materials and in high-temperature applications prone to adhesive wear and built-up edge (BUE) formation. Furthermore, high-temperature studies confirmed that the in-house coating retained a low H/E ratio, high plasticity index, high toughness, and low roughness, without compromising the hardness or elastic modulus values. In contrast, the commercial coating failed to retain its properties at higher temperatures. These high-temperature studies provide valuable insights for selecting CrN coatings tailored for machining materials that tend to adhere to the cutting tool and for high-temperature applications.
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 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 | 0.001 |
| 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) | 0.008 | 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 teacher head, 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".