Residual stress depth profiles self-developed in cathodic arc deposited Ti-Al-N coatings prepared at different constant substrate bias values
Bibliographic record
Abstract
Ti-Al-N coatings were prepared by cathodic arc deposition on Inconel 718 substrates at different values of constant substrate bias voltage, aiming to produce samples with different self-developed residual stress (RS) depth profiles through the thickness of the coatings. RS profile measurements and structural characterization were performed on a laboratory-scale x-ray diffraction system (x-ray energy of 8 keV) and in a synchrotron x-ray radiation facility (x-ray energy of 15 keV). Mechanical testing to obtain hardness and Young’s modulus values was performed by instrumented nanoindentation. The results indicate higher compressive RS at the film/substrate interface that decays to lower compressive stress or mild tensile stress at the film surface. Surface hardness and the compressive RS value of the coating increase with larger values of the substrate bias voltage. By comparing the stress characterization done on a laboratory scale and at the synchrotron facility, one observes a generally good agreement, indicating that these analyses may be conducted at a smaller scale and with less costly equipment, and still maintain a reliable precision. The work presents and reviews in detail the methodology of the RS depth-profile analysis. The highest hardness of 31.1 GPa and near-substrate compressive RS around −10 GPa were obtained for a bias of −100 V. Transmission electron microscopy results indicate that regions with higher compressive stresses are found to have smaller columns and denser structure, while portions of the same sample with mild compressive or tensile stresses present larger column size and are richer in hexagonal phases. The findings demonstrate the complex interplay between stress, microstructure, and ultimately mechanical properties in industrially produced Ti-Al-N coatings and indicate that any successful strategy to mitigate stress development should consider the inhomogeneous self-developed stress gradients present even in coatings deposited under constant and controlled conditions.
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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 | 0.000 | 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) | 0.001 | 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".