A Comparative Study on Al0.6Ti0.4N Coatings Deposited by Cathodic Arc and HiPIMS in End Milling of Stainless Steel 316L
Bibliographic record
Abstract
The machining of austenitic stainless steel alloys is usually characterized by high levels of adhesion and built-up edge; therefore, improving tribological conditions is fundamental to obtaining higher tool life and better surface finish. In this work, three different Al0.6Ti0.4N coatings are compared, two deposited by Cathodic Arc Evaporation (CAE) and one with High-Power Impulse Magnetron Sputtering (HiPIMS). The effects of the micromechanical properties and the microstructure of the coatings were then studied and related to the machining performance. Both arc-deposited coatings (CAE 1 and 2) exhibited similar average tool life, 127 min and 128 min, respectively. Whereas the HiPIMS lasted for only 21.2 min, the HiPIMS-coated tool had a much shorter tool life (more than six times lower than both CAE coatings) due to the intense adhesion that occurred in the early stages of the tool life. This higher adhesion ultimately caused built-up edge and chipping of the tool. This was confirmed by the cutting forces and more deformation on the shear band and undersurface of the chips, which are related to higher levels of friction. The higher adhesion could be attributed to the columnar structure of the HiPIMS and the (111) main texture, which presents a higher surface energy when compared to the dominant (200) from both arc depositions. Studies focused on tribology are necessary to further understand this relationship. In terms of micromechanical properties, tools with the highest plasticity index performed better (CAE 2 = 0.544, CAE 1 = 0.532, and HiPIMS = 0.459). For interrupted cutting machining where adhesion is the main wear mechanism, a reserve of plasticity is beneficial to dissipate the energy generated during friction, even if this was related to lower hardness levels (CAE 2 = 26.6 GPa, CAE 1 = 29.9 GPa, and HiPIMS = 33.6 GPa), as the main wear mechanism was adhesive and not abrasive.
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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".